#load("vcomball20210902.Rda")
load(path(here::here("InitalDataCleaning/Data/vcomball20210902.Rda")))
d <- vcomball
# load("vsurvall20210902.Rda")
# d <- vsurvall

#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))

SITE ID

  • Codes(based on Surveyid)
    • 10 Greater CA
    • 20 Georgia
    • 25 North Carolina
    • 30 Northern CA
    • 40 Louisiana
    • 50 New Jersey
    • 60 Detroit
    • 61 Michigan
    • 70 Texas
    • 80 Los Angeles County
    • 81 USC-Other
    • 82 USC-MEC
    • 90 New York
    • 94 Florida
    • 95 WebRecruit-Limbo
    • 99 WebRecruit
  siteid <- as.factor(trimws(d[,"siteid"]))
  #new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
  
  levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
  levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
  levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
  levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
  levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
  levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
  levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
  levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
  levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
  levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
  levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
  levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
  levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"

  siteid_new<- siteid
  d<-data.frame(d, siteid_new)
  new.d <- data.frame(new.d, siteid)
  new.d <- apply_labels(new.d, siteid = "Site ID")
  new.d.1 <- data.frame(new.d.1, siteid)
  siteid_count<-count(new.d$siteid)
  colnames(siteid_count)<- c("Registry", "Total")
  kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]

SURVEY ID

  • Scantron assigned SurveyID
  surveyid <- as.factor(d[,"surveyid"])
  isDup <- duplicated(surveyid)
  numDups <- sum(isDup)
  dups <- surveyid[isDup]
  
  new.d <- data.frame(new.d, surveyid)
  new.d <- apply_labels(new.d, surveyid = "Survey ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## factor(0)
## 201 Levels: 300005  300006  300015  300020  300024  300025  300029  300033  300040  300041  300042  300046  ... 302064
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$surveyid)))
## [1] 0

LOCATION NAME

  • Name of Registry delivery location
  locationname <- as.factor(d[,"locationname"])
  
  new.d <- data.frame(new.d, locationname)
  new.d <- apply_labels(new.d, locationname = "Recruitment Location")
  temp.d <- data.frame (new.d, locationname)

  result<-questionr::freq(temp.d$locationname, total = TRUE)
  #Create a NICE table
  kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
n % val%
Greater Bay 201 100 100
Total 201 100 100

RESPOND ID

  • From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
  respondid <- as.factor(d[,"respondid"])
  #remove NAs in respondid in order to avoid showing NAs in duplicated values
  respondid_rm<-respondid[!is.na(respondid)]
  isDup <- duplicated(respondid_rm)
  numDups <- sum(isDup)
  dups <- respondid_rm[isDup]
  
  new.d <- data.frame(new.d, respondid)
  new.d <- apply_labels(new.d, respondid = "RESPOND ID")
  
  print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 2"
  print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
  print(dups)
## [1] 30100177 30100172
## 199 Levels: 30100003 30100006 30100030 30100033 30100036 30100037 30100038 30100041 30100044 30100048 ... 30100855
  print("Number of NAs:")
## [1] "Number of NAs:"
  print(sum(is.na(new.d$respondid)))
## [1] 0

METHODOLOGY

  • How survey was completed
    • P=Paper
    • O=Online complete
st_css()
  methodology <- as.factor(d[,"methodology"])
  levels(methodology) <- list(Paper="P",
                              Online="O")
  methodology <- ordered(methodology, c("Paper", "Online"))
  new.d <- data.frame(new.d, methodology)
  new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
  temp.d <- data.frame (new.d, methodology)  
  
  result<-questionr::freq(temp.d$methodology, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Paper 201 100 100
Online 0 0 0
Total 201 100 100

A1: Date of diagnosis

  • A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
  
  new.d <- data.frame(new.d, a1month)
  new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
  temp.d <- data.frame (new.d, a1month) 
  
  result<-questionr::freq(temp.d$a1month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
n % val%
1 13 6.5 7.6
10 18 9.0 10.6
11 15 7.5 8.8
12 7 3.5 4.1
2 17 8.5 10.0
3 19 9.5 11.2
4 10 5.0 5.9
5 11 5.5 6.5
6 21 10.4 12.4
7 15 7.5 8.8
8 14 7.0 8.2
9 10 5.0 5.9
NA 31 15.4 NA
Total 201 100.0 100.0 
  #count<-as.data.frame(table(new.d$a1month))
  #colnames(count)<- c("a1month", "Total")
  #freq1<-table(new.d$a1month)
  #freq<-as.data.frame(round(prop.table(freq1),3))
  #colnames(freq)<- c("a1month", "Freq")
  #result<-merge(count, freq,by="a1month",sort=F)
  #kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")

#a1year
  tmp<-d[,"a1year"]
  tmp[tmp=="15"]<-"2015"
  a1year <- as.factor(tmp)
  #levels(a1year)[levels(a1year)=="15"] <- "2015"
  #a1year[a1year=="15"] <- "2015"  # change "15" to "2015"
  #a1year <- as.Date(a1year, format = "%Y")
  #a1year <- relevel(a1year, ref="1914")

  new.d <- data.frame(new.d, a1year)
  new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
  temp.d <- data.frame (new.d, a1year) 

  result<-questionr::freq(temp.d$a1year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
n % val%
1916 2 1.0 1.1
1946 1 0.5 0.6
1950 1 0.5 0.6
2006 1 0.5 0.6
2007 1 0.5 0.6
2011 1 0.5 0.6
2012 3 1.5 1.7
2013 2 1.0 1.1
2014 8 4.0 4.6
2015 48 23.9 27.4
2016 47 23.4 26.9
2017 46 22.9 26.3
2018 7 3.5 4.0
2019 5 2.5 2.9
2020 1 0.5 0.6
2021 1 0.5 0.6
NA 26 12.9 NA
Total 201 100.0 100.0 
  #a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
  a1not <- as.factor(d[,"a1not"])
  levels(a1not) <- list(NEVER_had_ProstateCancer="1",
                         HAVE_had_ProstateCancer="2")
  new.d <- data.frame(new.d, a1not)
  new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
  temp.d <- data.frame (new.d, a1not) 

  result<-questionr::freq(temp.d$a1not, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
n % val%
NEVER_had_ProstateCancer 0 0 0
HAVE_had_ProstateCancer 201 100 100
Total 201 100 100

A2: Identify as AA

  • A2. Do you identify as Black or African American?
    • 2=Yes
    • 1=No
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
                   Yes="2")
  a2 <- ordered(a2, c("Yes","No"))
  
  new.d <- data.frame(new.d, a2)
  new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, a2) 
  
  result<-questionr::freq(temp.d$a2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A2")
A2
n % val%
Yes 183 91 100
No 0 0 0
NA 18 9 NA
Total 201 100 100

A3: Black or African American group

  • A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
    • A3_1: 1=Black/African American
    • A3_2: 1=Nigerian
    • A3_3: 1=Jamaican
    • A3_4: 1=Ethiopian
    • A3_5: 1=Haitian
    • A3_6: 1=Somali
    • a3_7: 1=Guyanese
    • A3_8: 1=Creole
    • A3_9: 1=West Indian
    • A3_10: 1=Caribbean
    • A3_11: 1=White
    • A3_12: 1=Asian/Asian American
    • A3_13: 1=Native American or American Indian or Alaskan Native
    • A3_14: 1=Middle Eastern or North African
    • A3_15: 1=Native Hawaiian or Pacific Islander
    • A3_16: 1=Hispanic
    • A3_17: 1=Latino
    • A3_18: 1=Spanish
    • A3_19: 1=Mexican/Mexican American
    • A3_20: 1=Salvadoran
    • A3_21: 1=Puerto Rican
    • A3_22: 1=Dominican
    • A3_23: 1=Columbian
    • A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
  levels(a3_1) <- list(Black_African_American="1")
  new.d <- data.frame(new.d, a3_1)
  new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
  temp.d <- data.frame (new.d, a3_1)
  result<-questionr::freq(temp.d$a3_1, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
n % val%
Black_African_American 184 91.5 100
NA 17 8.5 NA
Total 201 100.0 100 
a3_2 <- as.factor(d[,"a3_2"])
  levels(a3_2) <- list(Nigerian="1")
  new.d <- data.frame(new.d, a3_2)
  new.d <- apply_labels(new.d, a3_2 = "Nigerian")
  temp.d <- data.frame (new.d, a3_2)
  result<-questionr::freq(temp.d$a3_2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
n % val%
Nigerian 6 3 100
NA 195 97 NA
Total 201 100 100 
a3_3 <- as.factor(d[,"a3_3"])
  levels(a3_3) <- list(Jamaican="1")
  new.d <- data.frame(new.d, a3_3)
  new.d <- apply_labels(new.d, a3_3 = "Jamaican")
  temp.d <- data.frame (new.d, a3_3)
  result<-questionr::freq(temp.d$a3_3, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
n % val%
Jamaican 3 1.5 100
NA 198 98.5 NA
Total 201 100.0 100 
a3_4 <- as.factor(d[,"a3_4"])
  levels(a3_4) <- list(Ethiopian="1")
  new.d <- data.frame(new.d, a3_4)
  new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
  temp.d <- data.frame (new.d, a3_4)
  result<-questionr::freq(temp.d$a3_4, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
n % val%
Ethiopian 3 1.5 100
NA 198 98.5 NA
Total 201 100.0 100 
a3_5 <- as.factor(d[,"a3_5"])
  levels(a3_5) <- list(Haitian="1")
  new.d <- data.frame(new.d, a3_5)
  new.d <- apply_labels(new.d, a3_5 = "Haitian")
  temp.d <- data.frame (new.d, a3_5)
  result<-questionr::freq(temp.d$a3_5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
n % val%
Haitian 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_6 <- as.factor(d[,"a3_6"])
  levels(a3_6) <- list(Somali="1")
  new.d <- data.frame(new.d, a3_6)
  new.d <- apply_labels(new.d, a3_6 = "Somali")
  temp.d <- data.frame (new.d, a3_6)
  result<-questionr::freq(temp.d$a3_6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
n % val%
Somali 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_7 <- as.factor(d[,"a3_7"])
  levels(a3_7) <- list(Guyanese="1")
  new.d <- data.frame(new.d, a3_7)
  new.d <- apply_labels(new.d, a3_7 = "Guyanese")
  temp.d <- data.frame (new.d, a3_7)
  result<-questionr::freq(temp.d$a3_7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
n % val%
Guyanese 2 1 100
NA 199 99 NA
Total 201 100 100 
a3_8 <- as.factor(d[,"a3_8"])
  levels(a3_8) <- list(Creole="1")
  new.d <- data.frame(new.d, a3_8)
  new.d <- apply_labels(new.d, a3_8 = "Creole")
  temp.d <- data.frame (new.d, a3_8)
  result<-questionr::freq(temp.d$a3_8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
n % val%
Creole 4 2 100
NA 197 98 NA
Total 201 100 100 
a3_9 <- as.factor(d[,"a3_9"])
  levels(a3_9) <- list(West_Indian="1")
  new.d <- data.frame(new.d, a3_9)
  new.d <- apply_labels(new.d, a3_9 = "West_Indian")
  temp.d <- data.frame (new.d, a3_9)
  result<-questionr::freq(temp.d$a3_9, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
n % val%
West_Indian 7 3.5 100
NA 194 96.5 NA
Total 201 100.0 100 
a3_10 <- as.factor(d[,"a3_10"])
  levels(a3_10) <- list(Caribbean="1")
  new.d <- data.frame(new.d, a3_10)
  new.d <- apply_labels(new.d, a3_10 = "Caribbean")
  temp.d <- data.frame (new.d, a3_10)
  result<-questionr::freq(temp.d$a3_10, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
n % val%
Caribbean 4 2 100
NA 197 98 NA
Total 201 100 100 
a3_11 <- as.factor(d[,"a3_11"])
  levels(a3_11) <- list(White="1")
  new.d <- data.frame(new.d, a3_11)
  new.d <- apply_labels(new.d, a3_11 = "White")
  temp.d <- data.frame (new.d, a3_11)
  result<-questionr::freq(temp.d$a3_11, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
n % val%
White 5 2.5 100
NA 196 97.5 NA
Total 201 100.0 100 
a3_12 <- as.factor(d[,"a3_12"])
  levels(a3_12) <- list(Asian="1")
  new.d <- data.frame(new.d, a3_12)
  new.d <- apply_labels(new.d, a3_12 = "Asian")
  temp.d <- data.frame (new.d, a3_12)
  result<-questionr::freq(temp.d$a3_12, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
n % val%
Asian 2 1 100
NA 199 99 NA
Total 201 100 100 
a3_13 <- as.factor(d[,"a3_13"])
  levels(a3_13) <- list(Native_Indian="1")
  new.d <- data.frame(new.d, a3_13)
  new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
  temp.d <- data.frame (new.d, a3_13)
  result<-questionr::freq(temp.d$a3_13, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
n % val%
Native_Indian 10 5 100
NA 191 95 NA
Total 201 100 100 
a3_14 <- as.factor(d[,"a3_14"])
  levels(a3_14) <- list(Middle_Eastern_North_African="1")
  new.d <- data.frame(new.d, a3_14)
  new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
  temp.d <- data.frame (new.d, a3_14)
  result<-questionr::freq(temp.d$a3_14, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
n % val%
Middle_Eastern_North_African 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_15 <- as.factor(d[,"a3_15"])
  levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
  new.d <- data.frame(new.d, a3_15)
  new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
  temp.d <- data.frame (new.d, a3_15)
  result<-questionr::freq(temp.d$a3_15, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
n % val%
Native_Hawaiian_Pacific_Islander 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
a3_16 <- as.factor(d[,"a3_16"])
  levels(a3_16) <- list(Hispanic="1")
  new.d <- data.frame(new.d, a3_16)
  new.d <- apply_labels(new.d, a3_16 = "Hispanic")
  temp.d <- data.frame (new.d, a3_16)
  result<-questionr::freq(temp.d$a3_16, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
n % val%
Hispanic 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_17 <- as.factor(d[,"a3_17"])
  levels(a3_17) <- list(Latino="1")
  new.d <- data.frame(new.d, a3_17)
  new.d <- apply_labels(new.d, a3_17 = "Latino")
  temp.d <- data.frame (new.d, a3_17)
  result<-questionr::freq(temp.d$a3_17, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
n % val%
Latino 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
a3_18 <- as.factor(d[,"a3_18"])
  levels(a3_18) <- list(Spanish="1")
  new.d <- data.frame(new.d, a3_18)
  new.d <- apply_labels(new.d, a3_18 = "Spanish")
  temp.d <- data.frame (new.d, a3_18)
  result<-questionr::freq(temp.d$a3_18, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
n % val%
Spanish 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_19 <- as.factor(d[,"a3_19"])
  levels(a3_19) <- list(Mexican="1")
  new.d <- data.frame(new.d, a3_19)
  new.d <- apply_labels(new.d, a3_19 = "Mexican")
  temp.d <- data.frame (new.d, a3_19)
  result<-questionr::freq(temp.d$a3_19, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
n % val%
Mexican 2 1 100
NA 199 99 NA
Total 201 100 100 
a3_20 <- as.factor(d[,"a3_20"])
  levels(a3_20) <- list(Salvadoran="1")
  new.d <- data.frame(new.d, a3_20)
  new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
  temp.d <- data.frame (new.d, a3_20)
  result<-questionr::freq(temp.d$a3_20, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
n % val%
Salvadoran 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_21 <- as.factor(d[,"a3_21"])
  levels(a3_21) <- list(Puerto_Rican="1")
  new.d <- data.frame(new.d, a3_21)
  new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
  temp.d <- data.frame (new.d, a3_21)
  result<-questionr::freq(temp.d$a3_21, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
n % val%
Puerto_Rican 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
a3_22 <- as.factor(d[,"a3_22"])
  levels(a3_22) <- list(Dominican="1")
  new.d <- data.frame(new.d, a3_22)
  new.d <- apply_labels(new.d, a3_22 = "Dominican")
  temp.d <- data.frame (new.d, a3_22)
  result<-questionr::freq(temp.d$a3_22, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
n % val%
Dominican 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_23 <- as.factor(d[,"a3_23"])
  levels(a3_23) <- list(Columbian="1")
  new.d <- data.frame(new.d, a3_23)
  new.d <- apply_labels(new.d, a3_23 = "Columbian")
  temp.d <- data.frame (new.d, a3_23)
  result<-questionr::freq(temp.d$a3_23, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
n % val%
Columbian 0 0 NaN
NA 201 100 NA
Total 201 100 100 
a3_24 <- as.factor(d[,"a3_24"])
  levels(a3_23) <- list(Other="1")
  new.d <- data.frame(new.d, a3_24)
  new.d <- apply_labels(new.d, a3_24 = "Other")
  temp.d <- data.frame (new.d, a3_24)
  result<-questionr::freq(temp.d$a3_24, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "24. Other")
24. Other
n % val%
1 2 1 100
NA 199 99 NA
Total 201 100 100

A3 Other: Black or African American group

a3other <- d[,"a3other"]
  new.d <- data.frame(new.d, a3other)
  new.d <- apply_labels(new.d, a3other = "A3Other")
  temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A3Other")
A3Other
n % val%
Both African-American and Creole. 1 0.5 16.7
Cameroon 1 0.5 16.7
Fathers family 1 0.5 16.7
Ghanaian/US citizen 1 0.5 16.7
None. 1 0.5 16.7
Trinidad 1 0.5 16.7
NA 195 97.0 NA
Total 201 100.0 100.0

A4: Month and year of birth

A4. What is your month and year of birth?

# a4month
a4month <- as.factor(d[,"a4month"])
  new.d <- data.frame(new.d, a4month)
  new.d <- apply_labels(new.d, a4month = "Month of birth")
  temp.d <- data.frame (new.d, a4month) 
  
  result<-questionr::freq(temp.d$a4month, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
n % val%
1 18 9.0 9.0
10 22 10.9 10.9
11 22 10.9 10.9
12 26 12.9 12.9
2 9 4.5 4.5
3 15 7.5 7.5
4 9 4.5 4.5
5 15 7.5 7.5
6 22 10.9 10.9
7 11 5.5 5.5
8 17 8.5 8.5
9 15 7.5 7.5
Total 201 100.0 100.0 
#a4year
a4year <- as.factor(d[,"a4year"])
  new.d <- data.frame(new.d, a4year)
  new.d <- apply_labels(new.d, a4year = "Year of birth")
  temp.d <- data.frame (new.d, a4year) 

  result<-questionr::freq(temp.d$a4year, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
n % val%
1937 1 0.5 0.5
1938 1 0.5 0.5
1940 1 0.5 0.5
1941 2 1.0 1.0
1942 6 3.0 3.0
1943 6 3.0 3.0
1944 5 2.5 2.5
1945 6 3.0 3.0
1946 9 4.5 4.5
1947 9 4.5 4.5
1948 7 3.5 3.5
1949 13 6.5 6.5
1950 13 6.5 6.5
1951 17 8.5 8.5
1952 7 3.5 3.5
1953 13 6.5 6.5
1954 15 7.5 7.5
1955 5 2.5 2.5
1956 5 2.5 2.5
1957 11 5.5 5.5
1958 13 6.5 6.5
1959 9 4.5 4.5
1960 4 2.0 2.0
1961 1 0.5 0.5
1962 1 0.5 0.5
1963 2 1.0 1.0
1964 1 0.5 0.5
1965 2 1.0 1.0
1966 5 2.5 2.5
1967 2 1.0 1.0
1968 2 1.0 1.0
1969 5 2.5 2.5
1970 1 0.5 0.5
1976 1 0.5 0.5
Total 201 100.0 100.0

A5: Where were you born

  • A5. Where were you born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a5)
  new.d <- apply_labels(new.d, a5 = "Born place")
  temp.d <- data.frame (new.d, a5) 
  
  result<-questionr::freq(temp.d$a5, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
n % val%
US 184 91.5 91.5
Africa 10 5.0 5.0
Cuba_Caribbean 3 1.5 1.5
Other 4 2.0 2.0
Total 201 100.0 100.0

A5 Other: Where were you born

a5other <- d[,"a5other"]
  new.d <- data.frame(new.d, a5other)
  new.d <- apply_labels(new.d, a5other = "a5other")
  temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A5Other")
A5Other
n % val%
British Guiana, South America 1 0.5 20
Chatroux, France 1 0.5 20
Guyana, South America 1 0.5 20
London England 1 0.5 20
Nigeria. 1 0.5 20
NA 196 97.5 NA
Total 201 100.0 100

A6: Biological father born

  • A6. Where was your biological father born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a6)
  new.d <- apply_labels(new.d, a6 = "Born place")
  temp.d <- data.frame (new.d, a6) 
  
  result<-questionr::freq(temp.d$a6, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
n % val%
US 180 89.6 90.9
Africa 10 5.0 5.1
Cuba_Caribbean 3 1.5 1.5
Other 5 2.5 2.5
NA 3 1.5 NA
Total 201 100.0 100.0

A6 Other: Biological father born

a6other <- d[,"a6other"]
  new.d <- data.frame(new.d, a6other)
  new.d <- apply_labels(new.d, a6other = "a6other")
  temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A6Other")
A6Other
n % val%
Bombay. 1 0.5 14.3
British Guiana 1 0.5 14.3
Guyana, South America 1 0.5 14.3
Jamaica 1 0.5 14.3
Nigeria. 1 0.5 14.3
Panama 1 0.5 14.3
Unknown 1 0.5 14.3
NA 194 96.5 NA
Total 201 100.0 100.0

A7: Biological mother born

  • A7. Where was your biological mother born?
    • 1=United States (includes Hawaii and US territories)
    • 2=Africa
    • 3=Cuba or Caribbean Islands
    • 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
                   Africa="2",
                   Cuba_Caribbean= "3",
                   Other="4")
  a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
  
  new.d <- data.frame(new.d, a7)
  new.d <- apply_labels(new.d, a7 = "Born place")
  temp.d <- data.frame (new.d, a7) 
  
  result<-questionr::freq(temp.d$a7, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
n % val%
US 185 92.0 92.0
Africa 10 5.0 5.0
Cuba_Caribbean 3 1.5 1.5
Other 3 1.5 1.5
Total 201 100.0 100.0

A7 Other: Biological father born

a7other <- d[,"a7other"]
  new.d <- data.frame(new.d, a7other)
  new.d <- apply_labels(new.d, a7other = "a7other")
  temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A7Other")
A7Other
n % val%
British Guiana 1 0.5 25
Guyana, South America 1 0.5 25
Jamaica 1 0.5 25
Nigeria. 1 0.5 25
NA 197 98.0 NA
Total 201 100.0 100

A8: Years lived in the US

  • A8. How many years have you lived in the United States?
    • 1=15 years or less
    • 2=16-25 years
    • 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
                   years_16_25="2",
                   more_than_25_or_whole_life= "3")
  a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
  
  new.d <- data.frame(new.d, a8)
  new.d <- apply_labels(new.d, a8 = "Years lived in the US")
  temp.d <- data.frame (new.d, a8) 
  
  result<-questionr::freq(temp.d$a8, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "A8")
A8
n % val%
less_or_15 3 1.5 1.5
years_16_25 5 2.5 2.6
more_than_25_or_whole_life 188 93.5 95.9
NA 5 2.5 NA
Total 201 100.0 100.0

B1A: Father

  • B1Aa: Father: Has this person had prostate cancer?
  • B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
  • B1Ac: Father: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Aa: Father: Has this person had prostate cancer?
  b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
  levels(b1aa) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1aa)
  new.d <- apply_labels(new.d, b1aa = "Father")
  temp.d <- data.frame (new.d, b1aa)  
  
  result<-questionr::freq(temp.d$b1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
n % val%
No 114 56.7 59.7
Yes 43 21.4 22.5
Dont_know 34 16.9 17.8
NA 10 5.0 NA
Total 201 100.0 100.0 
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55? 
  b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
  levels(b1ab) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ab)
  new.d <- apply_labels(new.d, b1ab = "Father")
  temp.d <- data.frame (new.d, b1ab)  
  
  result<-questionr::freq(temp.d$b1ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 44 21.9 65.7
Yes 6 3.0 9.0
Dont_know 17 8.5 25.4
NA 134 66.7 NA
Total 201 100.0 100.0 
#B1Ac: Father: Did he (or any) die of prostate cancer?
  b1ac <- as.factor(d[,"b1ac"])
  # Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
  levels(b1ac) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ac)
  new.d <- apply_labels(new.d, b1ac = "Father")
  temp.d <- data.frame (new.d, b1ac)  
  
  result<-questionr::freq(temp.d$b1ac,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
n % val%
No 42 20.9 61.8
Yes 14 7.0 20.6
Dont_know 12 6.0 17.6
NA 133 66.2 NA
Total 201 100.0 100.0

B1B: Any Brother

  • B1BNo: Any Brother
    • 1=I had no brothers
    • if not marked
  • B1Ba: Any Brother: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ba2: Any Brother: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Bc: Any Brother: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo: Any Brother
  b1bno <- as.factor(d[,"b1bno"])
  levels(b1bno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1bno)
  new.d <- apply_labels(new.d, b1bno = "Any Brother")
  temp.d <- data.frame (new.d, b1bno)  
  
  result<-questionr::freq(temp.d$b1bno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
n % val%
No_brothers 30 14.9 100
NA 171 85.1 NA
Total 201 100.0 100 
#B1Ba: Any Brother: Has this person had prostate cancer? 
  b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
  levels(b1ba) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ba)
  new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
  temp.d <- data.frame (new.d, b1ba)  
  
  result<-questionr::freq(temp.d$b1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
n % val%
No 118 58.7 69.4
Yes 31 15.4 18.2
Dont_know 21 10.4 12.4
NA 31 15.4 NA
Total 201 100.0 100.0 
#B1Ba2: Any Brother: If Yes, number with prostate cancer
  b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
  levels(b1ba2) <- list(One="1",
                     Two_or_more="2")
  b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ba2)
  new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
  temp.d <- data.frame (new.d, b1ba2)  
  
  result<-questionr::freq(temp.d$b1ba2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
n % val%
One 19 9.5 82.6
Two_or_more 4 2.0 17.4
NA 178 88.6 NA
Total 201 100.0 100.0 
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
  b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
  levels(b1bb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bb)
  new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
  temp.d <- data.frame (new.d, b1bb)  
  
  result<-questionr::freq(temp.d$b1bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 38 18.9 63.3
Yes 7 3.5 11.7
Dont_know 15 7.5 25.0
NA 141 70.1 NA
Total 201 100.0 100.0 
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
  b1bc <- as.factor(d[,"b1bc"])
  # Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
  levels(b1bc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1bc)
  new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
  temp.d <- data.frame (new.d, b1bc)  
  
  result<-questionr::freq(temp.d$b1bc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
n % val%
No 47 23.4 81.0
Yes 1 0.5 1.7
Dont_know 10 5.0 17.2
NA 143 71.1 NA
Total 201 100.0 100.0

B1C: Any Son

  • B1CNo: Any Son
    • 1=I had no sons
    • if not marked
  • B1Ca: Any Son: Has this person had prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Ca2: Any Son: If Yes, number with prostate cancer
    • 1=1
    • 2=2+
  • B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
    • 1=No
    • 2=Yes
    • 88=Don’t know
  • B1Cc: Any Son: Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1BNo
  b1cno <- as.factor(d[,"b1cno"])
  levels(b1cno) <- list(No_brothers="1")

  new.d <- data.frame(new.d, b1cno)
  new.d <- apply_labels(new.d, b1cno = "Any Son")
  temp.d <- data.frame (new.d, b1cno)  
  
  result<-questionr::freq(temp.d$b1cno,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
n % val%
No_brothers 45 22.4 100
NA 156 77.6 NA
Total 201 100.0 100 
#B1Ca
  b1ca <- as.factor(d[,"b1ca"])
  # Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
  levels(b1ca) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ca)
  new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
  temp.d <- data.frame (new.d, b1ca)  
  
  result<-questionr::freq(temp.d$b1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
n % val%
No 137 68.2 94.5
Yes 3 1.5 2.1
Dont_know 5 2.5 3.4
NA 56 27.9 NA
Total 201 100.0 100.0 
#B1Ca2
  b1ca2 <- as.factor(d[,"b1ca2"])
  # Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
  levels(b1ca2) <- list(One="1",
                     Two_or_more="2")
  b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
  
  new.d <- data.frame(new.d, b1ca2)
  new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
  temp.d <- data.frame (new.d, b1ca2)  
  
  result<-questionr::freq(temp.d$b1ca2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
n % val%
One 3 1.5 42.9
Two_or_more 4 2.0 57.1
NA 194 96.5 NA
Total 201 100.0 100.0 
#B1Cb
  b1cb <- as.factor(d[,"b1cb"])
  # Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
  levels(b1cb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cb)
  new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
  temp.d <- data.frame (new.d, b1cb)  
  
  result<-questionr::freq(temp.d$b1cb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
n % val%
No 24 11.9 80.0
Yes 1 0.5 3.3
Dont_know 5 2.5 16.7
NA 171 85.1 NA
Total 201 100.0 100.0 
#B1Cc
  b1cc <- as.factor(d[,"b1cc"])
  # Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
  levels(b1cc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1cc)
  new.d <- apply_labels(new.d, b1cc = "Any Son: die")
  temp.d <- data.frame (new.d, b1cc)  
  
  result<-questionr::freq(temp.d$b1cc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
n % val%
No 26 12.9 89.7
Yes 1 0.5 3.4
Dont_know 2 1.0 6.9
NA 172 85.6 NA
Total 201 100.0 100.0

B1D: Maternal Grandfather

  • B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  • B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  • b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
  b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
  levels(b1da) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1da <- ordered(b1da, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1da)
  new.d <- apply_labels(new.d, b1da = "Father")
  temp.d <- data.frame (new.d, b1da)  
  
  result<-questionr::freq(temp.d$b1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
n % val%
No 72 35.8 38.7
Yes 7 3.5 3.8
Dont_know 107 53.2 57.5
NA 15 7.5 NA
Total 201 100.0 100.0 
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
  b1db <- as.factor(d[,"b1db"])
  # Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
  levels(b1db) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1db <- ordered(b1db, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1db)
  new.d <- apply_labels(new.d, b1db = "Father")
  temp.d <- data.frame (new.d, b1db)  
  
  result<-questionr::freq(temp.d$b1db,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 18 9.0 43.9
Yes 2 1.0 4.9
Dont_know 21 10.4 51.2
NA 160 79.6 NA
Total 201 100.0 100.0 
# B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?
  b1dc <- as.factor(d[,"b1dc"])
  # Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
  levels(b1dc) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1dc)
  new.d <- apply_labels(new.d, b1dc = "Father")
  temp.d <- data.frame (new.d, b1dc)  
  
  result<-questionr::freq(temp.d$b1dc,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s  side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
n % val%
No 18 9.0 43.9
Yes 2 1.0 4.9
Dont_know 21 10.4 51.2
NA 160 79.6 NA
Total 201 100.0 100.0

B1E: Paternal Grandfather

  • B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
  • B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  • B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
    • 1=No
    • 2=Yes
    • 88=Don’t know
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer? 
  b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
  levels(b1ea) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ea)
  new.d <- apply_labels(new.d, b1ea = "Father")
  temp.d <- data.frame (new.d, b1ea)  
  
  result<-questionr::freq(temp.d$b1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
n % val%
No 61 30.3 32.6
Yes 11 5.5 5.9
Dont_know 115 57.2 61.5
NA 14 7.0 NA
Total 201 100.0 100.0 
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
  b1eb <- as.factor(d[,"b1eb"])
  # Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
  levels(b1eb) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1eb)
  new.d <- apply_labels(new.d, b1eb = "Father")
  temp.d <- data.frame (new.d, b1eb)  
  
  result<-questionr::freq(temp.d$b1eb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
n % val%
No 16 8.0 33.3
Yes 4 2.0 8.3
Dont_know 28 13.9 58.3
NA 153 76.1 NA
Total 201 100.0 100.0 
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
  b1ec <- as.factor(d[,"b1ec"])
  # Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
  levels(b1ec) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, b1ec)
  new.d <- apply_labels(new.d, b1ec = "Father")
  temp.d <- data.frame (new.d, b1ec)  
  
  result<-questionr::freq(temp.d$b1ec,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
n % val%
No 17 8.5 36.2
Yes 6 3.0 12.8
Dont_know 24 11.9 51.1
NA 154 76.6 NA
Total 201 100.0 100.0

B2: Family History (Other cancers)

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
    • 2=Yes
    • 1=No
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
                   Yes="2")
  b2 <- ordered(b2, c("Yes","No"))
  
  new.d <- data.frame(new.d, b2)
  new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
  temp.d <- data.frame (new.d, b2) 
  
  result<-questionr::freq(temp.d$b2, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B2")
B2
n % val%
Yes 31 15.4 28.4
No 78 38.8 71.6
NA 92 45.8 NA
Total 201 100.0 100.0

B2A: Mother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2A_1: 1=Breast
    • B2A_2: 1=Ovarian
    • B2A_3: 1=Colorectal
    • B2A_4: 1=Lung
    • B2A_5: 1=Other Cancer
  b2a_1 <- as.factor(d[,"b2a_1"])
  levels(b2a_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2a_1)
  new.d <- apply_labels(new.d, b2a_1 = "Breast")
  temp.d <- data.frame (new.d, b2a_1)  
  result<-questionr::freq(temp.d$b2a_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 17 8.5 100
NA 184 91.5 NA
Total 201 100.0 100 
  b2a_2 <- as.factor(d[,"b2a_2"])
  levels(b2a_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2a_2)
  new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2a_2)  
  result<-questionr::freq(temp.d$b2a_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 7 3.5 100
NA 194 96.5 NA
Total 201 100.0 100 
  b2a_3 <- as.factor(d[,"b2a_3"])
  levels(b2a_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2a_3)
  new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2a_3)  
  
  result<-questionr::freq(temp.d$b2a_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 5 2.5 100
NA 196 97.5 NA
Total 201 100.0 100 
  b2a_4 <- as.factor(d[,"b2a_4"])
  levels(b2a_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2a_4)
  new.d <- apply_labels(new.d, b2a_4 = "Lung")
  temp.d <- data.frame (new.d, b2a_4)  
  
  result<-questionr::freq(temp.d$b2a_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 6 3 100
NA 195 97 NA
Total 201 100 100 
  b2a_5 <- as.factor(d[,"b2a_5"])
  levels(b2a_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2a_5)
  new.d <- apply_labels(new.d, b2a_5 = "Lung")
  temp.d <- data.frame (new.d, b2a_5)  
  
  result<-questionr::freq(temp.d$b2a_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 12 6 100
NA 189 94 NA
Total 201 100 100

B2B: Father

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2B_1: 1=Breast
    • B2B_3: 1=Colorectal
    • B2B_4: 1=Lung
    • B2B_5: 1=Other Cancer
  b2b_1 <- as.factor(d[,"b2b_1"])
  levels(b2b_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2b_1)
  new.d <- apply_labels(new.d, b2b_1 = "Breast")
  temp.d <- data.frame (new.d, b2b_1)  
  result<-questionr::freq(temp.d$b2b_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2b_3 <- as.factor(d[,"b2b_3"])
  levels(b2b_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2b_3)
  new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2b_3)  
  
  result<-questionr::freq(temp.d$b2b_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 4 2 100
NA 197 98 NA
Total 201 100 100 
  b2b_4 <- as.factor(d[,"b2b_4"])
  levels(b2b_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2b_4)
  new.d <- apply_labels(new.d, b2b_4 = "Lung")
  temp.d <- data.frame (new.d, b2b_4)  
  
  result<-questionr::freq(temp.d$b2b_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 12 6 100
NA 189 94 NA
Total 201 100 100 
  b2b_5 <- as.factor(d[,"b2b_5"])
  levels(b2b_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2b_5)
  new.d <- apply_labels(new.d, b2b_5 = "Lung")
  temp.d <- data.frame (new.d, b2b_5)  
  
  result<-questionr::freq(temp.d$b2b_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 14 7 100
NA 187 93 NA
Total 201 100 100

B2C: Any sister

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2C_1: 1=Breast
    • B2C_2: 1=Ovarian
    • B2C_3: 1=Colorectal
    • B2C_4: 1=Lung
    • B2C_5: 1=Other Cancer
  b2c_1 <- as.factor(d[,"b2c_1"])
  levels(b2c_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2c_1)
  new.d <- apply_labels(new.d, b2c_1 = "Breast")
  temp.d <- data.frame (new.d, b2c_1)  
  result<-questionr::freq(temp.d$b2c_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 13 6.5 100
NA 188 93.5 NA
Total 201 100.0 100 
  b2c_2 <- as.factor(d[,"b2c_2"])
  levels(b2c_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2c_2)
  new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2c_2)  
  result<-questionr::freq(temp.d$b2c_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 5 2.5 100
NA 196 97.5 NA
Total 201 100.0 100 
  b2c_3 <- as.factor(d[,"b2c_3"])
  levels(b2c_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2c_3)
  new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2c_3)  
  
  result<-questionr::freq(temp.d$b2c_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
  b2c_4 <- as.factor(d[,"b2c_4"])
  levels(b2c_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2c_4)
  new.d <- apply_labels(new.d, b2c_4 = "Lung")
  temp.d <- data.frame (new.d, b2c_4)  
  
  result<-questionr::freq(temp.d$b2c_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
  b2c_5 <- as.factor(d[,"b2c_5"])
  levels(b2c_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2c_5)
  new.d <- apply_labels(new.d, b2c_5 = "Lung")
  temp.d <- data.frame (new.d, b2c_5)  
  
  result<-questionr::freq(temp.d$b2c_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 7 3.5 100
NA 194 96.5 NA
Total 201 100.0 100

B2D: Any brother

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2D_1: 1=Breast
    • B2D_3: 1=Colorectal
    • B2D_4: 1=Lung
    • B2D_5: 1=Other Cancer
  b2d_1 <- as.factor(d[,"b2d_1"])
  levels(b2d_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2d_1)
  new.d <- apply_labels(new.d, b2d_1 = "Breast")
  temp.d <- data.frame (new.d, b2d_1)  
  result<-questionr::freq(temp.d$b2d_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2d_3 <- as.factor(d[,"b2d_3"])
  levels(b2d_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2d_3)
  new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2d_3)  
  
  result<-questionr::freq(temp.d$b2d_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
  b2d_4 <- as.factor(d[,"b2d_4"])
  levels(b2d_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2d_4)
  new.d <- apply_labels(new.d, b2d_4 = "Lung")
  temp.d <- data.frame (new.d, b2d_4)  
  
  result<-questionr::freq(temp.d$b2d_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 4 2 100
NA 197 98 NA
Total 201 100 100 
  b2d_5 <- as.factor(d[,"b2d_5"])
  levels(b2d_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2d_5)
  new.d <- apply_labels(new.d, b2d_5 = "Lung")
  temp.d <- data.frame (new.d, b2d_5)  
  
  result<-questionr::freq(temp.d$b2d_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 15 7.5 100
NA 186 92.5 NA
Total 201 100.0 100

B2E: Any daughter

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2E_1: 1=Breast
    • B2E_2: 1=Ovarian
    • B2E_3: 1=Colorectal
    • B2E_4: 1=Lung
    • B2E_5: 1=Other Cancer
  b2e_1 <- as.factor(d[,"b2e_1"])
  levels(b2e_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2e_1)
  new.d <- apply_labels(new.d, b2e_1 = "Breast")
  temp.d <- data.frame (new.d, b2e_1)  
  result<-questionr::freq(temp.d$b2e_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2e_2 <- as.factor(d[,"b2e_2"])
  levels(b2e_2) <- list(Ovarian="1")
  new.d <- data.frame(new.d, b2e_2)
  new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
  temp.d <- data.frame (new.d, b2e_2)  
  result<-questionr::freq(temp.d$b2e_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
n % val%
Ovarian 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2e_3 <- as.factor(d[,"b2e_3"])
  levels(b2e_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2e_3)
  new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2e_3)  
  
  result<-questionr::freq(temp.d$b2e_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2e_4 <- as.factor(d[,"b2e_4"])
  levels(b2e_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2e_4)
  new.d <- apply_labels(new.d, b2e_4 = "Lung")
  temp.d <- data.frame (new.d, b2e_4)  
  
  result<-questionr::freq(temp.d$b2e_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2e_5 <- as.factor(d[,"b2e_5"])
  levels(b2e_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2e_5)
  new.d <- apply_labels(new.d, b2e_5 = "Lung")
  temp.d <- data.frame (new.d, b2e_5)  
  
  result<-questionr::freq(temp.d$b2e_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 2 1 100
NA 199 99 NA
Total 201 100 100

B2F: Any son

  • B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
    • B2F_1: 1=Breast
    • B2F_3: 1=Colorectal
    • B2F_4: 1=Lung
    • B2F_5: 1=Other Cancer
  b2f_1 <- as.factor(d[,"b2f_1"])
  levels(b2f_1) <- list(Breast="1")
  new.d <- data.frame(new.d, b2f_1)
  new.d <- apply_labels(new.d, b2f_1 = "Breast")
  temp.d <- data.frame (new.d, b2f_1)  
  result<-questionr::freq(temp.d$b2f_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
n % val%
Breast 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2f_3 <- as.factor(d[,"b2f_3"])
  levels(b2f_3) <- list(Colorectal="1")
  new.d <- data.frame(new.d, b2f_3)
  new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
  temp.d <- data.frame (new.d, b2f_3)  
  
  result<-questionr::freq(temp.d$b2f_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
n % val%
Colorectal 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2f_4 <- as.factor(d[,"b2f_4"])
  levels(b2f_4) <- list(Lung="1")
  new.d <- data.frame(new.d, b2f_4)
  new.d <- apply_labels(new.d, b2f_4 = "Lung")
  temp.d <- data.frame (new.d, b2f_4)  
  
  result<-questionr::freq(temp.d$b2f_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
n % val%
Lung 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  b2f_5 <- as.factor(d[,"b2f_5"])
  levels(b2f_5) <- list(Other_Cancer="1")
  new.d <- data.frame(new.d, b2f_5)
  new.d <- apply_labels(new.d, b2f_5 = "Lung")
  temp.d <- data.frame (new.d, b2f_5)  
  
  result<-questionr::freq(temp.d$b2f_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
n % val%
Other_Cancer 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100

B3: Current health

  • B3. In general, how would you rate your current health?
    • 1=Excellent
    • 2=Very Good
    • 3=Good
    • 4=Fair
    • 5=Poor
  b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
  levels(b3) <- list(Excellent="1",
                     Very_Good="2",
                     Good="3",
                     Fair="4",
                     Poor="5")
  b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))

  new.d <- data.frame(new.d, b3)
  new.d <- apply_labels(new.d, b3 = "Current Health")
  temp.d <- data.frame (new.d, b3)  
  
  result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Excellent 12 6.0 6.4 6.0 6.4
Very_Good 50 24.9 26.6 30.8 33.0
Good 86 42.8 45.7 73.6 78.7
Fair 38 18.9 20.2 92.5 98.9
Poor 2 1.0 1.1 93.5 100.0
NA 13 6.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

B4: Comorbidities

  • B4. Has the doctor ever told you that you have/had…
    • Heart Attack
    • Heart Failure or CHF
    • Stroke
    • Hypertension
    • Peripheral arterial disease
    • High Cholesterol
    • Asthma, COPD
    • Stomach ulcers
    • Crohn’s Disease
    • Diabetes
    • Kidney Problems
    • Cirrhosis, liver damage
    • Arthritis
    • Dementia
    • Depression
    • AIDS
    • Other Cancer
# Heart Attack
  b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
  levels(b4aa) <- list(No="1",
                     Yes="2")
  b4aa <- ordered(b4aa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4aa)
  new.d <- apply_labels(new.d, b4aa = "Heart Attack")
  temp.d <- data.frame (new.d, b4aa)  
  
  result<-questionr::freq(temp.d$b4aa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
n % val%
No 178 88.6 96.2
Yes 7 3.5 3.8
NA 16 8.0 NA
Total 201 100.0 100.0 
  b4ab <- as.factor(d[,"b4ab"])
  new.d <- data.frame(new.d, b4ab)
  new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
  temp.d <- data.frame (new.d, b4ab)  
  result<-questionr::freq(temp.d$b4ab, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
n % val%
47 1 0.5 16.7
50 2 1.0 33.3
52 1 0.5 16.7
60 2 1.0 33.3
NA 195 97.0 NA
Total 201 100.0 100.0 
# Heart Failure or CHF
  b4ba <- as.factor(d[,"b4ba"])
  # Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
  levels(b4ba) <- list(No="1",
                     Yes="2")
  b4ba <- ordered(b4ba, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ba)
  new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
  temp.d <- data.frame (new.d, b4ba)  
  
  result<-questionr::freq(temp.d$b4ba, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
n % val%
No 175 87.1 93.1
Yes 13 6.5 6.9
NA 13 6.5 NA
Total 201 100.0 100.0 
  b4bb <- as.factor(d[,"b4bb"])
  new.d <- data.frame(new.d, b4bb)
  new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
  temp.d <- data.frame (new.d, b4bb)  
  result<-questionr::freq(temp.d$b4bb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
n % val%
30 1 0.5 9.1
4 1 0.5 9.1
43 1 0.5 9.1
55 1 0.5 9.1
59 1 0.5 9.1
60 2 1.0 18.2
67 1 0.5 9.1
68 1 0.5 9.1
75 1 0.5 9.1
78 1 0.5 9.1
NA 190 94.5 NA
Total 201 100.0 100.0 
# Stroke  
  b4ca <- as.factor(d[,"b4ca"])
  # Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
  levels(b4ca) <- list(No="1",
                     Yes="2")
  b4ca <- ordered(b4ca, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ca)
  new.d <- apply_labels(new.d, b4ca = "Stroke")
  temp.d <- data.frame (new.d, b4ca)  
  
  result<-questionr::freq(temp.d$b4ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
n % val%
No 178 88.6 94.7
Yes 10 5.0 5.3
NA 13 6.5 NA
Total 201 100.0 100.0 
  b4cb <- as.factor(d[,"b4cb"])
  new.d <- data.frame(new.d, b4cb)
  new.d <- apply_labels(new.d, b4cb = "Stroke age")
  temp.d <- data.frame (new.d, b4cb)  
  result<-questionr::freq(temp.d$b4cb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stroke Age")
Stroke Age
n % val%
55 2 1.0 22.2
60 2 1.0 22.2
61 3 1.5 33.3
66 2 1.0 22.2
NA 192 95.5 NA
Total 201 100.0 100.0 
# Hypertension 
  b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
  levels(b4da) <- list(No="1",
                     Yes="2")
  b4da <- ordered(b4da, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4da)
  new.d <- apply_labels(new.d, b4da = "Hypertension")
  temp.d <- data.frame (new.d, b4da)  
  
  result<-questionr::freq(temp.d$b4da, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
n % val%
No 64 31.8 34.4
Yes 122 60.7 65.6
NA 15 7.5 NA
Total 201 100.0 100.0 
  b4db <- as.factor(d[,"b4db"])
  new.d <- data.frame(new.d, b4db)
  new.d <- apply_labels(new.d, b4db = "Hypertension age")
  temp.d <- data.frame (new.d, b4db)  
  result<-questionr::freq(temp.d$b4db, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
n % val%
15 1 0.5 0.9
2 1 0.5 0.9
28 1 0.5 0.9
29 1 0.5 0.9
30 3 1.5 2.7
31 1 0.5 0.9
35 4 2.0 3.5
37 2 1.0 1.8
40 10 5.0 8.8
41 2 1.0 1.8
43 2 1.0 1.8
44 1 0.5 0.9
45 5 2.5 4.4
46 2 1.0 1.8
47 3 1.5 2.7
48 3 1.5 2.7
50 18 9.0 15.9
51 1 0.5 0.9
52 2 1.0 1.8
53 1 0.5 0.9
54 1 0.5 0.9
55 8 4.0 7.1
57 2 1.0 1.8
58 4 2.0 3.5
59 3 1.5 2.7
60 15 7.5 13.3
61 3 1.5 2.7
62 2 1.0 1.8
63 1 0.5 0.9
64 1 0.5 0.9
65 1 0.5 0.9
66 1 0.5 0.9
67 1 0.5 0.9
69 1 0.5 0.9
7 1 0.5 0.9
70 1 0.5 0.9
72 1 0.5 0.9
74 1 0.5 0.9
97 1 0.5 0.9
NA 88 43.8 NA
Total 201 100.0 100.0 
# Peripheral arterial disease 
  b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"  
  levels(b4ea) <- list(No="1",
                     Yes="2")
  b4ea <- ordered(b4ea, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ea)
  new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
  temp.d <- data.frame (new.d, b4ea)  
  
  result<-questionr::freq(temp.d$b4ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
n % val%
No 178 88.6 96.2
Yes 7 3.5 3.8
NA 16 8.0 NA
Total 201 100.0 100.0 
  b4eb <- as.factor(d[,"b4eb"])
  new.d <- data.frame(new.d, b4eb)
  new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
  temp.d <- data.frame (new.d, b4eb)  
  result<-questionr::freq(temp.d$b4eb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
n % val%
49 1 0.5 10
50 1 0.5 10
51 1 0.5 10
55 2 1.0 20
60 2 1.0 20
64 2 1.0 20
98 1 0.5 10
NA 191 95.0 NA
Total 201 100.0 100 
# High Cholesterol 
  b4fa <- as.factor(d[,"b4fa"])
  # Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
  levels(b4fa) <- list(No="1",
                     Yes="2")
  b4fa <- ordered(b4fa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4fa)
  new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
  temp.d <- data.frame (new.d, b4fa)  
  
  result<-questionr::freq(temp.d$b4fa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
n % val%
No 101 50.2 57.1
Yes 76 37.8 42.9
NA 24 11.9 NA
Total 201 100.0 100.0 
  b4fb <- as.factor(d[,"b4fb"])
  new.d <- data.frame(new.d, b4fb)
  new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
  temp.d <- data.frame (new.d, b4fb)  
  result<-questionr::freq(temp.d$b4fb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
n % val%
15 1 0.5 1.4
25 1 0.5 1.4
27 1 0.5 1.4
35 2 1.0 2.8
40 4 2.0 5.6
41 2 1.0 2.8
45 4 2.0 5.6
48 1 0.5 1.4
49 1 0.5 1.4
50 8 4.0 11.3
51 2 1.0 2.8
52 2 1.0 2.8
54 1 0.5 1.4
55 9 4.5 12.7
56 2 1.0 2.8
57 3 1.5 4.2
59 1 0.5 1.4
60 13 6.5 18.3
61 2 1.0 2.8
62 1 0.5 1.4
63 2 1.0 2.8
66 1 0.5 1.4
68 1 0.5 1.4
7 1 0.5 1.4
70 3 1.5 4.2
72 1 0.5 1.4
75 1 0.5 1.4
NA 130 64.7 NA
Total 201 100.0 100.0 
#  Asthma, COPD
  b4ga <- as.factor(d[,"b4ga"])
  # Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
  levels(b4ga) <- list(No="1",
                     Yes="2")
  b4ga <- ordered(b4ga, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ga)
  new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
  temp.d <- data.frame (new.d, b4ga)  
  
  result<-questionr::freq(temp.d$b4ga, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
n % val%
No 159 79.1 80.7
Yes 38 18.9 19.3
NA 4 2.0 NA
Total 201 100.0 100.0 
  b4gb <- as.factor(d[,"b4gb"])
  new.d <- data.frame(new.d, b4gb)
  new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
  temp.d <- data.frame (new.d, b4gb)  
  result<-questionr::freq(temp.d$b4gb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
n % val%
10 2 1.0 5.6
11 1 0.5 2.8
13 1 0.5 2.8
2 1 0.5 2.8
20 1 0.5 2.8
25 1 0.5 2.8
27 1 0.5 2.8
30 1 0.5 2.8
33 1 0.5 2.8
35 1 0.5 2.8
4 1 0.5 2.8
40 1 0.5 2.8
45 1 0.5 2.8
5 2 1.0 5.6
50 3 1.5 8.3
54 1 0.5 2.8
57 1 0.5 2.8
59 1 0.5 2.8
60 3 1.5 8.3
62 1 0.5 2.8
63 1 0.5 2.8
64 1 0.5 2.8
65 2 1.0 5.6
67 1 0.5 2.8
71 1 0.5 2.8
75 1 0.5 2.8
77 1 0.5 2.8
9 2 1.0 5.6
NA 165 82.1 NA
Total 201 100.0 100.0 
# Stomach ulcers
  b4ha <- as.factor(d[,"b4ha"])
  # Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
  levels(b4ha) <- list(No="1",
                     Yes="2")
  b4ha <- ordered(b4ha, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ha)
  new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
  temp.d <- data.frame (new.d, b4ha)  
  
  result<-questionr::freq(temp.d$b4ha, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
n % val%
No 182 90.5 93.8
Yes 12 6.0 6.2
NA 7 3.5 NA
Total 201 100.0 100.0 
  b4hb <- as.factor(d[,"b4hb"])
  new.d <- data.frame(new.d, b4hb)
  new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
  temp.d <- data.frame (new.d, b4hb)  
  result<-questionr::freq(temp.d$b4hb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
n % val%
13 2 1.0 18.2
20 1 0.5 9.1
25 2 1.0 18.2
35 1 0.5 9.1
42 1 0.5 9.1
45 1 0.5 9.1
58 1 0.5 9.1
60 1 0.5 9.1
65 1 0.5 9.1
NA 190 94.5 NA
Total 201 100.0 100.0 
# Crohn's Disease
  b4ia <- as.factor(d[,"b4ia"])
  # Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
  levels(b4ia) <- list(No="1",
                     Yes="2")
  b4ia <- ordered(b4ia, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ia)
  new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
  temp.d <- data.frame (new.d, b4ia)  
  
  result<-questionr::freq(temp.d$b4ia, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
n % val%
No 184 91.5 95.3
Yes 9 4.5 4.7
NA 8 4.0 NA
Total 201 100.0 100.0 
  b4ib <- as.factor(d[,"b4ib"])
  new.d <- data.frame(new.d, b4ib)
  new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
  temp.d <- data.frame (new.d, b4ib)  
  result<-questionr::freq(temp.d$b4ib, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
n % val%
44 1 0.5 12.5
45 1 0.5 12.5
48 1 0.5 12.5
51 1 0.5 12.5
55 1 0.5 12.5
65 1 0.5 12.5
70 1 0.5 12.5
76 1 0.5 12.5
NA 193 96.0 NA
Total 201 100.0 100.0 
# Diabetes
  b4ja <- as.factor(d[,"b4ja"])
  # Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
  levels(b4ja) <- list(No="1",
                     Yes="2")
  b4ja <- ordered(b4ja, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ja)
  new.d <- apply_labels(new.d, b4ja = "Diabetes")
  temp.d <- data.frame (new.d, b4ja)  
  
  result<-questionr::freq(temp.d$b4ja, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
n % val%
No 149 74.1 76
Yes 47 23.4 24
NA 5 2.5 NA
Total 201 100.0 100 
  b4jb <- as.factor(d[,"b4jb"])
  new.d <- data.frame(new.d, b4jb)
  new.d <- apply_labels(new.d, b4jb = "Diabetes age")
  temp.d <- data.frame (new.d, b4jb)  
  result<-questionr::freq(temp.d$b4jb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
Diabetes Age
n % val%
20 1 0.5 2.4
30 1 0.5 2.4
35 2 1.0 4.8
44 1 0.5 2.4
45 3 1.5 7.1
50 4 2.0 9.5
51 1 0.5 2.4
52 1 0.5 2.4
53 1 0.5 2.4
55 5 2.5 11.9
56 4 2.0 9.5
57 1 0.5 2.4
58 2 1.0 4.8
59 1 0.5 2.4
60 5 2.5 11.9
61 3 1.5 7.1
63 1 0.5 2.4
64 2 1.0 4.8
65 1 0.5 2.4
69 1 0.5 2.4
75 1 0.5 2.4
NA 159 79.1 NA
Total 201 100.0 100.0 
# Kidney Problems
  b4ka <- as.factor(d[,"b4ka"])
  # Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
  levels(b4ka) <- list(No="1",
                     Yes="2")
  b4ka <- ordered(b4ka, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ka)
  new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
  temp.d <- data.frame (new.d, b4ka)  
  
  result<-questionr::freq(temp.d$b4ka, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
n % val%
No 183 91.0 93.4
Yes 13 6.5 6.6
NA 5 2.5 NA
Total 201 100.0 100.0 
  b4kb <- as.factor(d[,"b4kb"])
  new.d <- data.frame(new.d, b4kb)
  new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
  temp.d <- data.frame (new.d, b4kb)  
  result<-questionr::freq(temp.d$b4kb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
n % val%
55 1 0.5 16.7
60 1 0.5 16.7
66 2 1.0 33.3
70 1 0.5 16.7
83 1 0.5 16.7
NA 195 97.0 NA
Total 201 100.0 100.0 
# Cirrhosis, liver damage
  b4la <- as.factor(d[,"b4la"])
  # Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
  levels(b4la) <- list(No="1",
                     Yes="2")
  b4la <- ordered(b4la, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4la)
  new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
  temp.d <- data.frame (new.d, b4la)  
  
  result<-questionr::freq(temp.d$b4la, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
n % val%
No 186 92.5 95.4
Yes 9 4.5 4.6
NA 6 3.0 NA
Total 201 100.0 100.0 
  b4lb <- as.factor(d[,"b4lb"])
  new.d <- data.frame(new.d, b4lb)
  new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
  temp.d <- data.frame (new.d, b4lb)  
  result<-questionr::freq(temp.d$b4lb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
n % val%
21 1 0.5 25
54 1 0.5 25
68 2 1.0 50
NA 197 98.0 NA
Total 201 100.0 100 
# Arthritis
  b4ma <- as.factor(d[,"b4ma"])
  # Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
  levels(b4ma) <- list(No="1",
                     Yes="2")
  b4ma <- ordered(b4ma, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4ma)
  new.d <- apply_labels(new.d, b4ma = "Arthritis")
  temp.d <- data.frame (new.d, b4ma)  
  
  result<-questionr::freq(temp.d$b4ma, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
n % val%
No 173 86.1 88.3
Yes 23 11.4 11.7
NA 5 2.5 NA
Total 201 100.0 100.0 
  b4mb <- as.factor(d[,"b4mb"])
  new.d <- data.frame(new.d, b4mb)
  new.d <- apply_labels(new.d, b4mb = "Arthritis age")
  temp.d <- data.frame (new.d, b4mb)  
  result<-questionr::freq(temp.d$b4mb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
Arthritis Age
n % val%
36 1 0.5 5
46 1 0.5 5
50 3 1.5 15
54 1 0.5 5
55 2 1.0 10
58 1 0.5 5
59 2 1.0 10
60 1 0.5 5
63 3 1.5 15
64 1 0.5 5
65 2 1.0 10
75 1 0.5 5
77 1 0.5 5
NA 181 90.0 NA
Total 201 100.0 100 
# Dementia
  b4na <- as.factor(d[,"b4na"])
  # Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
  levels(b4na) <- list(No="1",
                     Yes="2")
  b4na <- ordered(b4na, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4na)
  new.d <- apply_labels(new.d, b4na = "Dementia")
  temp.d <- data.frame (new.d, b4na)  
  
  result<-questionr::freq(temp.d$b4na, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
n % val%
No 191 95 97.9
Yes 4 2 2.1
NA 6 3 NA
Total 201 100 100.0 
  b4nb <- as.factor(d[,"b4nb"])
  new.d <- data.frame(new.d, b4nb)
  new.d <- apply_labels(new.d, b4nb = "Dementia age")
  temp.d <- data.frame (new.d, b4nb)  
  result<-questionr::freq(temp.d$b4nb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Dementia Age")
Dementia Age
n % val%
70 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100 
# Depression 
  b4oa <- as.factor(d[,"b4oa"])
  # Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
  levels(b4oa) <- list(No="1",
                     Yes="2")
  b4oa <- ordered(b4oa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4oa)
  new.d <- apply_labels(new.d, b4oa = "Depression")
  temp.d <- data.frame (new.d, b4oa)  
  
  result<-questionr::freq(temp.d$b4oa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
n % val%
No 170 84.6 89
Yes 21 10.4 11
NA 10 5.0 NA
Total 201 100.0 100 
  b4ob <- as.factor(d[,"b4ob"])
  new.d <- data.frame(new.d, b4ob)
  new.d <- apply_labels(new.d, b4ob = "Depression age")
  temp.d <- data.frame (new.d, b4ob)  
  result<-questionr::freq(temp.d$b4ob, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Depression Age")
Depression Age
n % val%
19 1 0.5 5.9
20 1 0.5 5.9
28 1 0.5 5.9
43 1 0.5 5.9
48 2 1.0 11.8
50 2 1.0 11.8
51 1 0.5 5.9
55 1 0.5 5.9
6 1 0.5 5.9
60 2 1.0 11.8
62 1 0.5 5.9
63 1 0.5 5.9
70 1 0.5 5.9
72 1 0.5 5.9
NA 184 91.5 NA
Total 201 100.0 100.0 
# AIDS
  b4pa <- as.factor(d[,"b4pa"])
  # Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
  levels(b4pa) <- list(No="1",
                     Yes="2")
  b4pa <- ordered(b4pa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4pa)
  new.d <- apply_labels(new.d, b4pa = "AIDS")
  temp.d <- data.frame (new.d, b4pa)  
  
  result<-questionr::freq(temp.d$b4pa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
n % val%
No 185 92 94.9
Yes 10 5 5.1
NA 6 3 NA
Total 201 100 100.0 
  b4pb <- as.factor(d[,"b4pb"])
  new.d <- data.frame(new.d, b4pb)
  new.d <- apply_labels(new.d, b4pb = "AIDS age")
  temp.d <- data.frame (new.d, b4pb)  
  result<-questionr::freq(temp.d$b4pb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "AIDS Age")
AIDS Age
n % val%
40 1 0.5 14.3
48 2 1.0 28.6
49 1 0.5 14.3
50 1 0.5 14.3
55 1 0.5 14.3
7 1 0.5 14.3
NA 194 96.5 NA
Total 201 100.0 100.0 
# Other Cancer
  b4qa <- as.factor(d[,"b4qa"])
  # Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
  levels(b4qa) <- list(No="1",
                     Yes="2")
  b4qa <- ordered(b4qa, c("No", "Yes"))
  
  new.d <- data.frame(new.d, b4qa)
  new.d <- apply_labels(new.d, b4qa = "Other Cancer")
  temp.d <- data.frame (new.d, b4qa)  
  
  result<-questionr::freq(temp.d$b4qa, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
n % val%
No 170 84.6 93.9
Yes 11 5.5 6.1
NA 20 10.0 NA
Total 201 100.0 100.0 
  b4qb <- as.factor(d[,"b4qb"])
  new.d <- data.frame(new.d, b4qb)
  new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
  temp.d <- data.frame (new.d, b4qb)  
  result<-questionr::freq(temp.d$b4qb, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
n % val%
43 1 0.5 8.3
48 1 0.5 8.3
49 1 0.5 8.3
55 1 0.5 8.3
61 1 0.5 8.3
62 1 0.5 8.3
66 1 0.5 8.3
67 2 1.0 16.7
69 2 1.0 16.7
72 1 0.5 8.3
NA 189 94.0 NA
Total 201 100.0 100.0

B4Q Other Cancer

b4qother <- d[,"b4qother"]
  new.d <- data.frame(new.d, b4qother)
  new.d <- apply_labels(new.d, b4qother = "b4qother")
  temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B4Q Other
n % val%
Bone. 1 0.5 9.1
Colon and left and right kidney 1 0.5 9.1
Do not know dates on prior questions 1 0.5 9.1
Glast cancer 1 0.5 9.1
Kalsom 1 0.5 9.1
Kidney 1 0.5 9.1
Liver mass (7-11-14) 1 0.5 9.1
Neuroendocrine cancer 1 0.5 9.1
Prostate only 1 0.5 9.1
Stage 4 lung cancer 1 0.5 9.1
Throat 1 0.5 9.1
NA 190 94.5 NA
Total 201 100.0 100.0

B5: Routine care

  • B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
    • 1=Community health center or free clinic
    • 2=Hospital (not emergency)/ urgent care clinic
    • 3=Private doctor’s office
    • 4=Emergency room
    • 5=Veteran’s Affairs/VA
    • 6=Other type of location
  b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
  levels(b5) <- list(Community_center_free_clinic="1",
                     Hospital_urgent_care_clinic="2",
                     Private_Dr_office="3",
                     ER="4",
                     VA="5",
                     Other="6")
  b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
  
  new.d <- data.frame(new.d, b5)
  new.d <- apply_labels(new.d, b5 = "routine medical care")
  temp.d <- data.frame (new.d, b5)  
  
  result<-questionr::freq(temp.d$b5 ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val%
Community_center_free_clinic 19 9.5 10.4
Hospital_urgent_care_clinic 21 10.4 11.5
Private_Dr_office 135 67.2 73.8
ER 1 0.5 0.5
VA 4 2.0 2.2
Other 3 1.5 1.6
NA 18 9.0 NA
Total 201 100.0 100.0

B5 Other: Routine care

b5other <- d[,"b5other"]
  new.d <- data.frame(new.d, b5other)
  new.d <- apply_labels(new.d, b5other = "b5other")
  temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "B5 Other")
B5 Other
n % val%
Eastmont Medical Center 1 0.5 12.5
Kaiser 1 0.5 12.5
Santa Clara Family Health Plan. 1 0.5 12.5
Stanford Health Care-employer benefits 1 0.5 12.5
Stanford hospital Palo Alto 1 0.5 12.5
Tricity health care 1 0.5 12.5
UCSF Medical Center. 1 0.5 12.5
Yearly physical exams 1 0.5 12.5
NA 193 96.0 NA
Total 201 100.0 100.0

C1: Years lived at current address

  • C1. How many years have you lived in your current address?
    • 1=Less than 1 year
    • 2=1-5 years
    • 3=6-10 years
    • 4=11-15 years
    • 5=16-20 years
    • 6=21+ years
  c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
  levels(c1) <- list(Less_than_1_year="1",
                     years_1_5="2",
                     years_6_10="3",
                     years_11_15="4",
                     years_16_20="5",
                     years_21_more="6")
  c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
  
  new.d <- data.frame(new.d, c1)
  new.d <- apply_labels(new.d, c1 = "living period")
  temp.d <- data.frame (new.d, c1)  
  
  result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l')
n % val% %cum val%cum
Less_than_1_year 9 4.5 4.6 4.5 4.6
years_1_5 36 17.9 18.3 22.4 22.8
years_6_10 30 14.9 15.2 37.3 38.1
years_11_15 25 12.4 12.7 49.8 50.8
years_16_20 20 10.0 10.2 59.7 60.9
years_21_more 77 38.3 39.1 98.0 100.0
NA 4 2.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C2A: Feel safe walking in the neighborhood

    1. On average, I felt/feel safe walking in my neighborhood day or night.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
  levels(c2a1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a1)
  new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
  temp.d <- data.frame (new.d, c2a1)  
  
  c2a2 <- as.factor(d[,"c2a2"])
  # Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
  levels(c2a2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a2)
  new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2a2) 
  
  c2a3 <- as.factor(d[,"c2a3"])
  # Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
  levels(c2a3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2a3)
  new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2a3)
  
  result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 102 50.7 51.8 50.7 51.8
Agree 56 27.9 28.4 78.6 80.2
Neutral 28 13.9 14.2 92.5 94.4
Disagree 10 5.0 5.1 97.5 99.5
Strongly_Disagree 1 0.5 0.5 98.0 100.0
NA 4 2.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 97 48.3 51.1 48.3 51.1
Agree 54 26.9 28.4 75.1 79.5
Neutral 24 11.9 12.6 87.1 92.1
Disagree 10 5.0 5.3 92.0 97.4
Strongly_Disagree 5 2.5 2.6 94.5 100.0
NA 11 5.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 98 48.8 52.7 48.8 52.7
Agree 47 23.4 25.3 72.1 78.0
Neutral 26 12.9 14.0 85.1 91.9
Disagree 12 6.0 6.5 91.0 98.4
Strongly_Disagree 3 1.5 1.6 92.5 100.0
NA 15 7.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C2B: Violence

    1. Violence was/is not a problem in my neighborhood.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
  levels(c2b1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b1)
  new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
  temp.d <- data.frame (new.d, c2b1)  
  
  c2b2 <- as.factor(d[,"c2b2"])
  # Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
  levels(c2b2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b2)
  new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2b2) 
  
  c2b3 <- as.factor(d[,"c2b3"])
  # Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
  levels(c2b3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2b3)
  new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2b3)
  
  result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 84 41.8 42.6 41.8 42.6
Agree 49 24.4 24.9 66.2 67.5
Neutral 41 20.4 20.8 86.6 88.3
Disagree 13 6.5 6.6 93.0 94.9
Strongly_Disagree 10 5.0 5.1 98.0 100.0
NA 4 2.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 71 35.3 37.8 35.3 37.8
Agree 54 26.9 28.7 62.2 66.5
Neutral 37 18.4 19.7 80.6 86.2
Disagree 13 6.5 6.9 87.1 93.1
Strongly_Disagree 13 6.5 6.9 93.5 100.0
NA 13 6.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 60 29.9 32.4 29.9 32.4
Agree 54 26.9 29.2 56.7 61.6
Neutral 37 18.4 20.0 75.1 81.6
Disagree 22 10.9 11.9 86.1 93.5
Strongly_Disagree 12 6.0 6.5 92.0 100.0
NA 16 8.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C2C: Safe from crime

    1. My neighborhood was/is safe from crime.
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis)
      1. Childhood or young adult life (up to age 30)
      • 1=Strongly Agree
      • 2=Agree
      • 3=Neutral (neither agree nor disagree)
      • 4=Disagree
      • 5=Strongly Disagree
  c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
  levels(c2c1) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c1)
  new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
  temp.d <- data.frame (new.d, c2c1)  
  
  c2c2 <- as.factor(d[,"c2c2"])
  # Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
  levels(c2c2) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c2)
  new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
  temp.d <- data.frame (new.d, c2c2) 
  
  c2c3 <- as.factor(d[,"c2c3"])
  # Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
  levels(c2c3) <- list(Strongly_Agree="1",
                     Agree="2",
                     Neutral="3",
                     Disagree="4",
                     Strongly_Disagree="5")
  c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, c2c3)
  new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
  temp.d <- data.frame (new.d, c2c3)
  
  result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Strongly_Agree 53 26.4 27.6 26.4 27.6
Agree 54 26.9 28.1 53.2 55.7
Neutral 47 23.4 24.5 76.6 80.2
Disagree 29 14.4 15.1 91.0 95.3
Strongly_Disagree 9 4.5 4.7 95.5 100.0
NA 9 4.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
n % val% %cum val%cum
Strongly_Agree 48 23.9 26.1 23.9 26.1
Agree 61 30.3 33.2 54.2 59.2
Neutral 34 16.9 18.5 71.1 77.7
Disagree 28 13.9 15.2 85.1 92.9
Strongly_Disagree 13 6.5 7.1 91.5 100.0
NA 17 8.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Strongly_Agree 44 21.9 24.3 21.9 24.3
Agree 50 24.9 27.6 46.8 51.9
Neutral 47 23.4 26.0 70.1 77.9
Disagree 26 12.9 14.4 83.1 92.3
Strongly_Disagree 14 7.0 7.7 90.0 100.0
NA 20 10.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C3A: Traffic

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Traffic
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
  levels(c3a1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a1)
  new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3a1)  
  
  c3a2 <- as.factor(d[,"c3a2"])
  # Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
  levels(c3a2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a2)
  new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3a2) 
  
  c3a3 <- as.factor(d[,"c3a3"])
  # Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
  levels(c3a3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3a3)
  new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3a3)
  
  result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 112 55.7 57.7 55.7 57.7
Somewhat_serious 58 28.9 29.9 84.6 87.6
Very_serious 18 9.0 9.3 93.5 96.9
Dont_know 6 3.0 3.1 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 112 55.7 58.3 55.7 58.3
Somewhat_serious 56 27.9 29.2 83.6 87.5
Very_serious 14 7.0 7.3 90.5 94.8
Dont_know 10 5.0 5.2 95.5 100.0
NA 9 4.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 133 66.2 70.4 66.2 70.4
Somewhat_serious 31 15.4 16.4 81.6 86.8
Very_serious 8 4.0 4.2 85.6 91.0
Dont_know 17 8.5 9.0 94.0 100.0
NA 12 6.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C3B: Noise

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. A lot of noise
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
  levels(c3b1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b1)
  new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
  temp.d <- data.frame (new.d, c3b1)  
  
  c3b2 <- as.factor(d[,"c3b2"])
  # Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
  levels(c3b2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b2)
  new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
  temp.d <- data.frame (new.d, c3b2) 
  
  c3b3 <- as.factor(d[,"c3b3"])
  # Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
  levels(c3b3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3b3)
  new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
  temp.d <- data.frame (new.d, c3b3)
  
  result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 143 71.1 73.7 71.1 73.7
Somewhat_serious 42 20.9 21.6 92.0 95.4
Very_serious 8 4.0 4.1 96.0 99.5
Dont_know 1 0.5 0.5 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 127 63.2 66.1 63.2 66.1
Somewhat_serious 47 23.4 24.5 86.6 90.6
Very_serious 13 6.5 6.8 93.0 97.4
Dont_know 5 2.5 2.6 95.5 100.0
NA 9 4.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 134 66.7 70.5 66.7 70.5
Somewhat_serious 37 18.4 19.5 85.1 90.0
Very_serious 7 3.5 3.7 88.6 93.7
Dont_know 12 6.0 6.3 94.5 100.0
NA 11 5.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C3C: Trash and litter

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Trash and litter
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
  levels(c3c1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c1)
  new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
  temp.d <- data.frame (new.d, c3c1)  
  
  c3c2 <- as.factor(d[,"c3c2"])
  # Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
  levels(c3c2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c2)
  new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
  temp.d <- data.frame (new.d, c3c2) 
  
  c3c3 <- as.factor(d[,"c3c3"])
  # Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
  levels(c3c3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3c3)
  new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
  temp.d <- data.frame (new.d, c3c3)
  
  result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 143 71.1 73.7 71.1 73.7
Somewhat_serious 29 14.4 14.9 85.6 88.7
Very_serious 19 9.5 9.8 95.0 98.5
Dont_know 3 1.5 1.5 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 140 69.7 72.9 69.7 72.9
Somewhat_serious 37 18.4 19.3 88.1 92.2
Very_serious 10 5.0 5.2 93.0 97.4
Dont_know 5 2.5 2.6 95.5 100.0
NA 9 4.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 133 66.2 70.0 66.2 70.0
Somewhat_serious 34 16.9 17.9 83.1 87.9
Very_serious 12 6.0 6.3 89.1 94.2
Dont_know 11 5.5 5.8 94.5 100.0
NA 11 5.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C3D: Too much light at night

  • C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
    1. Too much light at night
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Non/Minor problem
      • 2=Somewhat serious problem
      • 3=Very serious problem
      • 88=Don’t Know
  c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
  levels(c3d1) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d1)
  new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
  temp.d <- data.frame (new.d, c3d1)  
  
  c3d2 <- as.factor(d[,"c3d2"])
  # Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
  levels(c3d2) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d2)
  new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
  temp.d <- data.frame (new.d, c3d2) 
  
  c3d3 <- as.factor(d[,"c3d3"])
  # Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
  levels(c3d3) <- list(Non_Minor="1",
                     Somewhat_serious="2",
                     Very_serious="3",
                     Dont_know="88")
  c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
  
  new.d <- data.frame(new.d, c3d3)
  new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
  temp.d <- data.frame (new.d, c3d3)
  
  result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Non_Minor 177 88.1 91.2 88.1 91.2
Somewhat_serious 10 5.0 5.2 93.0 96.4
Very_serious 0 0.0 0.0 93.0 96.4
Dont_know 7 3.5 3.6 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Non_Minor 166 82.6 86.9 82.6 86.9
Somewhat_serious 15 7.5 7.9 90.0 94.8
Very_serious 1 0.5 0.5 90.5 95.3
Dont_know 9 4.5 4.7 95.0 100.0
NA 10 5.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Non_Minor 164 81.6 86.8 81.6 86.8
Somewhat_serious 8 4.0 4.2 85.6 91.0
Very_serious 3 1.5 1.6 87.1 92.6
Dont_know 14 7.0 7.4 94.0 100.0
NA 12 6.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C4A: Neighbors talking outside

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
  levels(c4a1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a1)
  new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
  temp.d <- data.frame (new.d, c4a1)  
  
  c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA" 
  levels(c4a2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a2)
  new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
  temp.d <- data.frame (new.d, c4a2) 
  
  c4a3 <- as.factor(d[,"c4a3"])
  # Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
  levels(c4a3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4a3)
  new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
  temp.d <- data.frame (new.d, c4a3)
  
  result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 89 44.3 45.6 44.3 45.6
Sometimes 74 36.8 37.9 81.1 83.6
Rarely_Never 31 15.4 15.9 96.5 99.5
Dont_know 1 0.5 0.5 97.0 100.0
NA 6 3.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 91 45.3 47.2 45.3 47.2
Sometimes 80 39.8 41.5 85.1 88.6
Rarely_Never 22 10.9 11.4 96.0 100.0
Dont_know 0 0.0 0.0 96.0 100.0
NA 8 4.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 120 59.7 62.8 59.7 62.8
Sometimes 54 26.9 28.3 86.6 91.1
Rarely_Never 8 4.0 4.2 90.5 95.3
Dont_know 9 4.5 4.7 95.0 100.0
NA 10 5.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C4B: Neighbors watch out for each other

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How often do/did neighbors watch out for each other, such as calling if they see a problem?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
  levels(c4b1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b1)
  new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
  temp.d <- data.frame (new.d, c4b1)  
  
  c4b2 <- as.factor(d[,"c4b2"])
  # Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
  levels(c4b2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b2)
  new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
  temp.d <- data.frame (new.d, c4b2) 
  
  c4b3 <- as.factor(d[,"c4b3"])
  # Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
  levels(c4b3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4b3)
  new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
  temp.d <- data.frame (new.d, c4b3)
  
  result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 87 43.3 45.5 43.3 45.5
Sometimes 58 28.9 30.4 72.1 75.9
Rarely_Never 37 18.4 19.4 90.5 95.3
Dont_know 9 4.5 4.7 95.0 100.0
NA 10 5.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 76 37.8 40.4 37.8 40.4
Sometimes 67 33.3 35.6 71.1 76.1
Rarely_Never 36 17.9 19.1 89.1 95.2
Dont_know 9 4.5 4.8 93.5 100.0
NA 13 6.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 111 55.2 59.0 55.2 59.0
Sometimes 40 19.9 21.3 75.1 80.3
Rarely_Never 23 11.4 12.2 86.6 92.6
Dont_know 14 7.0 7.4 93.5 100.0
NA 13 6.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C4C: Neighbors know by name

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you know by name?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
  levels(c4c1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c1)
  new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4c1)  
  
  c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
  levels(c4c2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c2)
  new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4c2) 
  
  c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
  levels(c4c3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4c3)
  new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4c3)
  
  result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 54 26.9 27.8 26.9 27.8
Sometimes 86 42.8 44.3 69.7 72.2
Rarely_Never 50 24.9 25.8 94.5 97.9
Dont_know 4 2.0 2.1 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 56 27.9 29.6 27.9 29.6
Sometimes 89 44.3 47.1 72.1 76.7
Rarely_Never 41 20.4 21.7 92.5 98.4
Dont_know 3 1.5 1.6 94.0 100.0
NA 12 6.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 117 58.2 62.6 58.2 62.6
Sometimes 41 20.4 21.9 78.6 84.5
Rarely_Never 19 9.5 10.2 88.1 94.7
Dont_know 10 5.0 5.3 93.0 100.0
NA 14 7.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C4D: Friendly talks with neighbors

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors do/did you have a friendly talk with at least once a week?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
  levels(c4d1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d1)
  new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
  temp.d <- data.frame (new.d, c4d1)  
  
  c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
  levels(c4d2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d2)
  new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
  temp.d <- data.frame (new.d, c4d2) 
  
  c4d3 <- as.factor(d[,"c4d3"])
  # Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
  levels(c4d3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4d3)
  new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
  temp.d <- data.frame (new.d, c4d3)
  
  result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 38 18.9 20.0 18.9 20.0
Sometimes 63 31.3 33.2 50.2 53.2
Rarely_Never 88 43.8 46.3 94.0 99.5
Dont_know 1 0.5 0.5 94.5 100.0
NA 11 5.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 45 22.4 24.1 22.4 24.1
Sometimes 74 36.8 39.6 59.2 63.6
Rarely_Never 65 32.3 34.8 91.5 98.4
Dont_know 3 1.5 1.6 93.0 100.0
NA 14 7.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 85 42.3 45.9 42.3 45.9
Sometimes 57 28.4 30.8 70.6 76.8
Rarely_Never 32 15.9 17.3 86.6 94.1
Dont_know 11 5.5 5.9 92.0 100.0
NA 16 8.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

C4E: Ask neighbors for help

  • C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
    1. How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
      1. Current (from prostate cancer diagnosis to present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Often
      • 2=Sometimes
      • 3=Rarely/Never
      • 88=Don’t Know
  c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
  levels(c4e1) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e1)
  new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
  temp.d <- data.frame (new.d, c4e1)  
  
  c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
  levels(c4e2) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e2)
  new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
  temp.d <- data.frame (new.d, c4e2) 
  
  c4e3 <- as.factor(d[,"c4e3"])
  # Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
  levels(c4e3) <- list(Often="1",
                     Sometimes="2",
                     Rarely_Never="3",
                     Dont_know="88")
  c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
  
  new.d <- data.frame(new.d, c4e3)
  new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
  temp.d <- data.frame (new.d, c4e3)
  
  result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
n % val% %cum val%cum
Often 38 18.9 20.5 18.9 20.5
Sometimes 56 27.9 30.3 46.8 50.8
Rarely_Never 81 40.3 43.8 87.1 94.6
Dont_know 10 5.0 5.4 92.0 100.0
NA 16 8.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val% %cum val%cum
Often 41 20.4 22.4 20.4 22.4
Sometimes 69 34.3 37.7 54.7 60.1
Rarely_Never 62 30.8 33.9 85.6 94.0
Dont_know 11 5.5 6.0 91.0 100.0
NA 18 9.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
  kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val% %cum val%cum
Often 76 37.8 41.8 37.8 41.8
Sometimes 50 24.9 27.5 62.7 69.2
Rarely_Never 39 19.4 21.4 82.1 90.7
Dont_know 17 8.5 9.3 90.5 100.0
NA 19 9.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

D1: Treat you because of your race/ethnicity

  • D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
      1. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
      1. For unfair reasons, have you ever not been hired for a job?
      1. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
      1. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
      1. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
      1. Have you ever been unfairly denied a bank loan?
      1. Have you ever been unfairly treated when getting medical care?
      • 1=No
      • 2=Yes
    • If yes, How stressful was this experience?
      • 1=Not at all
      • 2=A little
      • 3=Somewhat
      • 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
  d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
  levels(d1aa) <- list(No="1",
                     Yes="2")
  d1aa <- ordered(d1aa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1aa)
  new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
  temp.d <- data.frame (new.d, d1aa)  
  
  d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA" 
  levels(d1ab) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1ab <- ordered(d1ab, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ab)
  new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
  temp.d <- data.frame (new.d, d1ab)
  
  result<-questionr::freq(temp.d$d1aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
n % val%
No 86 42.8 44.6
Yes 107 53.2 55.4
NA 8 4.0 NA
Total 201 100.0 100.0 
  result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# b. For unfair reasons, have you ever not been hired for a job?
  d1ba <- as.factor(d[,"d1ba"])
  # Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
  levels(d1ba) <- list(No="1",
                     Yes="2")
  d1ba <- ordered(d1ba, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ba)
  new.d <- apply_labels(new.d, d1ba = "not be hired")
  temp.d <- data.frame (new.d, d1ba)  
  
  d1bb <- as.factor(d[,"d1bb"])
  # Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
  levels(d1bb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1bb <- ordered(d1bb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1bb)
  new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
  temp.d <- data.frame (new.d, d1bb)
  
  result<-questionr::freq(temp.d$d1ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
n % val%
No 107 53.2 55.4
Yes 86 42.8 44.6
NA 8 4.0 NA
Total 201 100.0 100.0 
  result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
  d1ca <- as.factor(d[,"d1ca"])
  # Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
  levels(d1ca) <- list(No="1",
                     Yes="2")
  d1ca <- ordered(d1ca, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1ca)
  new.d <- apply_labels(new.d, d1ca = "By police")
  temp.d <- data.frame (new.d, d1ca)  
  
  result<-questionr::freq(temp.d$d1ca,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
n % val%
No 72 35.8 36.9
Yes 123 61.2 63.1
NA 6 3.0 NA
Total 201 100.0 100.0 
  d1cb <- as.factor(d[,"d1cb"])
  # Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
  levels(d1cb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1cb <- ordered(d1cb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1cb)
  new.d <- apply_labels(new.d, d1cb = "By police-stressful")
  temp.d <- data.frame (new.d, d1cb)
  result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
  d1da <- as.factor(d[,"d1da"])
  # Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
  levels(d1da) <- list(No="1",
                     Yes="2")
  d1da <- ordered(d1da, c( "No","Yes"))
  
  new.d <- data.frame(new.d, d1da)
  new.d <- apply_labels(new.d, d1da = "unfair education")
  temp.d <- data.frame (new.d, d1da)  
  
  result<-questionr::freq(temp.d$d1da,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
n % val%
No 139 69.2 72.8
Yes 52 25.9 27.2
NA 10 5.0 NA
Total 201 100.0 100.0 
  d1db <- as.factor(d[,"d1db"])
  # Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
  levels(d1db) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1db <- ordered(d1db, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1db)
  new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
  temp.d <- data.frame (new.d, d1db)
  result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
  d1ea <- as.factor(d[,"d1ea"])
  # Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
  levels(d1ea) <- list(No="1",
                     Yes="2")
  d1ea <- ordered(d1ea, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ea)
  new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
  temp.d <- data.frame (new.d, d1ea)  
  
  result<-questionr::freq(temp.d$d1ea,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
n % val%
No 140 69.7 74.1
Yes 49 24.4 25.9
NA 12 6.0 NA
Total 201 100.0 100.0 
  d1eb <- as.factor(d[,"d1eb"])
  # Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
  levels(d1eb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1eb <- ordered(d1eb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1eb)
  new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
  temp.d <- data.frame (new.d, d1eb)
  result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# f.   Have   you   ever   been   unfairly denied a bank loan?
  d1fa <- as.factor(d[,"d1fa"])
  # Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
  levels(d1fa) <- list(No="1",
                     Yes="2")
  d1fa <- ordered(d1fa, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fa)
  new.d <- apply_labels(new.d, d1fa = "Bank loan")
  temp.d <- data.frame (new.d, d1fa)  
  
  result<-questionr::freq(temp.d$d1fa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
n % val%
No 138 68.7 73
Yes 51 25.4 27
NA 12 6.0 NA
Total 201 100.0 100 
  d1fb <- as.factor(d[,"d1fb"])
  # Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
  levels(d1fb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1fb <- ordered(d1fb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1fb)
  new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
  temp.d <- data.frame (new.d, d1fb)
  result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100 
# g.   Have   you   ever   been   unfairly treated when getting medical care?
  d1ga <- as.factor(d[,"d1ga"])
  # Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
  levels(d1ga) <- list(No="1",
                     Yes="2")
  d1ga <- ordered(d1ga, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1ga)
  new.d <- apply_labels(new.d, d1ga = "unfair medical care")
  temp.d <- data.frame (new.d, d1ga)  
  
  result<-questionr::freq(temp.d$d1ga,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
n % val%
No 150 74.6 78.5
Yes 41 20.4 21.5
NA 10 5.0 NA
Total 201 100.0 100.0 
  d1gb <- as.factor(d[,"d1gb"])
  # Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
  levels(d1gb) <- list(Not_at_all="1",
                     A_little="2",
                     Somewhat="3",
                     Extremely="4")
  d1gb <- ordered(d1gb, c("No","Yes"))
  
  new.d <- data.frame(new.d, d1gb)
  new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
  temp.d <- data.frame (new.d, d1gb)
  result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
n % val% %cum val%cum
No 0 0 NaN 0 NaN
Yes 0 0 NaN 0 NaN
NA 201 100 NA 100 NA
Total 201 100 100 100 100

D2: Medical Mistrust

  • D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
  d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
  levels(d2a) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2a)
  new.d <- apply_labels(new.d, d2a = "deceived or misled")
  temp.d <- data.frame (new.d, d2a)  
  
  result<-questionr::freq(temp.d$d2a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
n % val%
Strongly_Agree 28 13.9 14.7
Somewhat_Agree 81 40.3 42.6
Somewhat_Disagree 41 20.4 21.6
Strongly_Disagree 40 19.9 21.1
NA 11 5.5 NA
Total 201 100.0 100.0 
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
  d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
  levels(d2b) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2b)
  new.d <- apply_labels(new.d, d2b = "personal affairs")
  temp.d <- data.frame (new.d, d2b)  
  
  result<-questionr::freq(temp.d$d2b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
n % val%
Strongly_Agree 20 10.0 10.4
Somewhat_Agree 59 29.4 30.7
Somewhat_Disagree 58 28.9 30.2
Strongly_Disagree 55 27.4 28.6
NA 9 4.5 NA
Total 201 100.0 100.0 
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
  d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
  levels(d2c) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2c)
  new.d <- apply_labels(new.d, d2c = "harmful experiments")
  temp.d <- data.frame (new.d, d2c)  
  
  result<-questionr::freq(temp.d$d2c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
n % val%
Strongly_Agree 41 20.4 21.8
Somewhat_Agree 54 26.9 28.7
Somewhat_Disagree 43 21.4 22.9
Strongly_Disagree 50 24.9 26.6
NA 13 6.5 NA
Total 201 100.0 100.0 
# d. Rich patients receive better care at hospitals than poor patients.
  d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
  levels(d2d) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2d)
  new.d <- apply_labels(new.d, d2d = "Rich patients better care")
  temp.d <- data.frame (new.d, d2d)  
  
  result<-questionr::freq(temp.d$d2d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
n % val%
Strongly_Agree 104 51.7 55.6
Somewhat_Agree 55 27.4 29.4
Somewhat_Disagree 14 7.0 7.5
Strongly_Disagree 14 7.0 7.5
NA 14 7.0 NA
Total 201 100.0 100.0 
# e. Male patients receive better care at hospitals than female patients.
  d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
  levels(d2e) <- list(Strongly_Agree="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d2e)
  new.d <- apply_labels(new.d, d2e = "Male patients better care")
  temp.d <- data.frame (new.d, d2e)  
  
  result<-questionr::freq(temp.d$d2e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
n % val%
Strongly_Agree 10 5.0 5.4
Somewhat_Agree 27 13.4 14.6
Somewhat_Disagree 79 39.3 42.7
Strongly_Disagree 69 34.3 37.3
NA 16 8.0 NA
Total 201 100.0 100.0

D3A: Treated with less respect

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been treated with less respect than other people
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
  levels(d3a1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a1)
  new.d <- apply_labels(new.d, d3a1 = "less respect-current")
  temp.d <- data.frame (new.d, d3a1)  
  
  result<-questionr::freq(temp.d$d3a1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 52 25.9 26.7
Rarely 49 24.4 25.1
Sometimes 73 36.3 37.4
Often 21 10.4 10.8
NA 6 3.0 NA
Total 201 100.0 100.0 
#2
  d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
  levels(d3a2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a2)
  new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
  temp.d <- data.frame (new.d, d3a2)  
  
  result<-questionr::freq(temp.d$d3a2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 39 19.4 20.7
Rarely 51 25.4 27.1
Sometimes 76 37.8 40.4
Often 22 10.9 11.7
NA 13 6.5 NA
Total 201 100.0 100.0 
#3
  d3a3 <- as.factor(d[,"d3a3"])
  # Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
  levels(d3a3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3a3)
  new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
  temp.d <- data.frame (new.d, d3a3)  
  
  result<-questionr::freq(temp.d$d3a3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 37 18.4 19.8
Rarely 45 22.4 24.1
Sometimes 64 31.8 34.2
Often 41 20.4 21.9
NA 14 7.0 NA
Total 201 100.0 100.0

D3B: Received poorer service

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have received poorer service than other people at restaurants or stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
  levels(d3b1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b1)
  new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
  temp.d <- data.frame (new.d, d3b1)  
  
  result<-questionr::freq(temp.d$d3b1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 38 18.9 19.5
Rarely 55 27.4 28.2
Sometimes 85 42.3 43.6
Often 17 8.5 8.7
NA 6 3.0 NA
Total 201 100.0 100.0 
#2
  d3b2 <- as.factor(d[,"d3b2"])
  # Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
  levels(d3b2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b2)
  new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
  temp.d <- data.frame (new.d, d3b2)  
  
  result<-questionr::freq(temp.d$d3b2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 28 13.9 15.0
Rarely 55 27.4 29.4
Sometimes 81 40.3 43.3
Often 23 11.4 12.3
NA 14 7.0 NA
Total 201 100.0 100.0 
#3
  d3b3 <- as.factor(d[,"d3b3"])
  # Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
  levels(d3b3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3b3)
  new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
  temp.d <- data.frame (new.d, d3b3)  
  
  result<-questionr::freq(temp.d$d3b3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 31 15.4 16.9
Rarely 41 20.4 22.4
Sometimes 78 38.8 42.6
Often 33 16.4 18.0
NA 18 9.0 NA
Total 201 100.0 100.0

D3C: Think you are not smart

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are not smart
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
  levels(d3c1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c1)
  new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
  temp.d <- data.frame (new.d, d3c1)  
  
  result<-questionr::freq(temp.d$d3c1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 40 19.9 20.3
Rarely 66 32.8 33.5
Sometimes 68 33.8 34.5
Often 23 11.4 11.7
NA 4 2.0 NA
Total 201 100.0 100.0 
#2
  d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
  levels(d3c2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c2)
  new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
  temp.d <- data.frame (new.d, d3c2)  
  
  result<-questionr::freq(temp.d$d3c2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 39 19.4 20.6
Rarely 66 32.8 34.9
Sometimes 60 29.9 31.7
Often 24 11.9 12.7
NA 12 6.0 NA
Total 201 100.0 100.0 
#3
  d3c3 <- as.factor(d[,"d3c3"])
  # Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
  levels(d3c3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3c3)
  new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
  temp.d <- data.frame (new.d, d3c3)  
  
  result<-questionr::freq(temp.d$d3c3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 39 19.4 21.0
Rarely 49 24.4 26.3
Sometimes 66 32.8 35.5
Often 32 15.9 17.2
NA 15 7.5 NA
Total 201 100.0 100.0

D3D: Be afraid of you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they are afraid of you
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
  levels(d3d1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d1)
  new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
  temp.d <- data.frame (new.d, d3d1)  
  
  result<-questionr::freq(temp.d$d3d1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 42 20.9 21.6
Rarely 54 26.9 27.8
Sometimes 74 36.8 38.1
Often 24 11.9 12.4
NA 7 3.5 NA
Total 201 100.0 100.0 
#2
  d3d2 <- as.factor(d[,"d3d2"])
  # Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
  levels(d3d2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d2)
  new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
  temp.d <- data.frame (new.d, d3d2)  
  
  result<-questionr::freq(temp.d$d3d2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 35 17.4 18.7
Rarely 53 26.4 28.3
Sometimes 71 35.3 38.0
Often 28 13.9 15.0
NA 14 7.0 NA
Total 201 100.0 100.0 
#3
  d3d3 <- as.factor(d[,"d3d3"])
  # Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
  levels(d3d3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3d3)
  new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
  temp.d <- data.frame (new.d, d3d3)  
  
  result<-questionr::freq(temp.d$d3d3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 44 21.9 23.9
Rarely 46 22.9 25.0
Sometimes 64 31.8 34.8
Often 30 14.9 16.3
NA 17 8.5 NA
Total 201 100.0 100.0

D3E: Think you are dishonest

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they think you are dishonest
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
  levels(d3e1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e1)
  new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
  temp.d <- data.frame (new.d, d3e1)  
  
  result<-questionr::freq(temp.d$d3e1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 61 30.3 31.0
Rarely 61 30.3 31.0
Sometimes 61 30.3 31.0
Often 14 7.0 7.1
NA 4 2.0 NA
Total 201 100.0 100.0 
#2
  d3e2 <- as.factor(d[,"d3e2"])
  # Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
  levels(d3e2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e2)
  new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
  temp.d <- data.frame (new.d, d3e2)  
  
  result<-questionr::freq(temp.d$d3e2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 53 26.4 28.0
Rarely 53 26.4 28.0
Sometimes 66 32.8 34.9
Often 17 8.5 9.0
NA 12 6.0 NA
Total 201 100.0 100.0 
#3
  d3e3 <- as.factor(d[,"d3e3"])
  # Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
  levels(d3e3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3e3)
  new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
  temp.d <- data.frame (new.d, d3e3)  
  
  result<-questionr::freq(temp.d$d3e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 50 24.9 26.7
Rarely 52 25.9 27.8
Sometimes 63 31.3 33.7
Often 22 10.9 11.8
NA 14 7.0 NA
Total 201 100.0 100.0

D3F: Better than you

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. People have acted as if they’re better than you are
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
  levels(d3f1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f1)
  new.d <- apply_labels(new.d, d3f1 = "better than you-current")
  temp.d <- data.frame (new.d, d3f1)  
  
  result<-questionr::freq(temp.d$d3f1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 27 13.4 13.8
Rarely 41 20.4 21.0
Sometimes 94 46.8 48.2
Often 33 16.4 16.9
NA 6 3.0 NA
Total 201 100.0 100.0 
#2
  d3f2 <- as.factor(d[,"d3f2"])
  # Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
  levels(d3f2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f2)
  new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
  temp.d <- data.frame (new.d, d3f2)  
  
  result<-questionr::freq(temp.d$d3f2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 24 11.9 12.8
Rarely 44 21.9 23.5
Sometimes 83 41.3 44.4
Often 36 17.9 19.3
NA 14 7.0 NA
Total 201 100.0 100.0 
#3
  d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
  levels(d3f3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3f3)
  new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
  temp.d <- data.frame (new.d, d3f3)  
  
  result<-questionr::freq(temp.d$d3f3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 24 11.9 12.9
Rarely 33 16.4 17.7
Sometimes 83 41.3 44.6
Often 46 22.9 24.7
NA 15 7.5 NA
Total 201 100.0 100.0

D3G: Insulted

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been called names or insulted
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
  levels(d3g1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g1)
  new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
  temp.d <- data.frame (new.d, d3g1)  
  
  result<-questionr::freq(temp.d$d3g1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 43 21.4 22.1
Rarely 79 39.3 40.5
Sometimes 66 32.8 33.8
Often 7 3.5 3.6
NA 6 3.0 NA
Total 201 100.0 100.0 
#2
  d3g2 <- as.factor(d[,"d3g2"])
  # Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
  levels(d3g2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g2)
  new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
  temp.d <- data.frame (new.d, d3g2)  
  
  result<-questionr::freq(temp.d$d3g2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 27 13.4 14.4
Rarely 76 37.8 40.6
Sometimes 71 35.3 38.0
Often 13 6.5 7.0
NA 14 7.0 NA
Total 201 100.0 100.0 
#3
  d3g3 <- as.factor(d[,"d3g3"])
  # Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
  levels(d3g3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3g3)
  new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
  temp.d <- data.frame (new.d, d3g3)  
  
  result<-questionr::freq(temp.d$d3g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 23 11.4 12.3
Rarely 51 25.4 27.3
Sometimes 79 39.3 42.2
Often 34 16.9 18.2
NA 14 7.0 NA
Total 201 100.0 100.0

D3H: Threatened or harassed

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been threatened or harassed
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
  levels(d3h1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h1)
  new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
  temp.d <- data.frame (new.d, d3h1)  
  
  result<-questionr::freq(temp.d$d3h1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 82 40.8 42.1
Rarely 73 36.3 37.4
Sometimes 36 17.9 18.5
Often 4 2.0 2.1
NA 6 3.0 NA
Total 201 100.0 100.0 
#2
  d3h2 <- as.factor(d[,"d3h2"])
  # Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
  levels(d3h2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h2)
  new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
  temp.d <- data.frame (new.d, d3h2)  
  
  result<-questionr::freq(temp.d$d3h2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 61 30.3 32.4
Rarely 70 34.8 37.2
Sometimes 48 23.9 25.5
Often 9 4.5 4.8
NA 13 6.5 NA
Total 201 100.0 100.0 
#3
  d3h3 <- as.factor(d[,"d3h3"])
  # Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
  levels(d3h3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3h3)
  new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
  temp.d <- data.frame (new.d, d3h3)  
  
  result<-questionr::freq(temp.d$d3h3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 48 23.9 25.7
Rarely 53 26.4 28.3
Sometimes 66 32.8 35.3
Often 20 10.0 10.7
NA 14 7.0 NA
Total 201 100.0 100.0

D3I: Followed around in stores

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. You have been followed around in stores
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
  levels(d3i1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i1)
  new.d <- apply_labels(new.d, d3i1 = "be followed-current")
  temp.d <- data.frame (new.d, d3i1)  
  
  result<-questionr::freq(temp.d$d3i1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 45 22.4 22.7
Rarely 60 29.9 30.3
Sometimes 71 35.3 35.9
Often 22 10.9 11.1
NA 3 1.5 NA
Total 201 100.0 100.0 
#2
  d3i2 <- as.factor(d[,"d3i2"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i2)
  new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
  temp.d <- data.frame (new.d, d3i2)  
  
  result<-questionr::freq(temp.d$d3i2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 34 16.9 17.9
Rarely 46 22.9 24.2
Sometimes 72 35.8 37.9
Often 38 18.9 20.0
NA 11 5.5 NA
Total 201 100.0 100.0 
#3
  d3i3 <- as.factor(d[,"d3i3"])
  # Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
  levels(d3i3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3i3)
  new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
  temp.d <- data.frame (new.d, d3i3)  
  
  result<-questionr::freq(temp.d$d3i3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 31 15.4 16.5
Rarely 32 15.9 17.0
Sometimes 67 33.3 35.6
Often 58 28.9 30.9
NA 13 6.5 NA
Total 201 100.0 100.0

D3J: How stressful

  • D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
    1. How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
      1. Current (from prostate cancer diagnosis to the present)
      1. Age 31 up to just before prostate cancer diagnosis
      1. Childhood or young adult life (up to age 30)
      • 1=Never
      • 2=Rarely
      • 3=Sometimes
      • 4=Often
# 1
  d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
  levels(d3j1) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j1)
  new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
  temp.d <- data.frame (new.d, d3j1)  
  
  result<-questionr::freq(temp.d$d3j1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
n % val%
Never 63 31.3 32.1
Rarely 67 33.3 34.2
Sometimes 47 23.4 24.0
Often 19 9.5 9.7
NA 5 2.5 NA
Total 201 100.0 100.0 
#2
  d3j2 <- as.factor(d[,"d3j2"])
  # Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
  levels(d3j2) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j2)
  new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
  temp.d <- data.frame (new.d, d3j2)  
  
  result<-questionr::freq(temp.d$d3j2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
n % val%
Never 52 25.9 27.8
Rarely 50 24.9 26.7
Sometimes 57 28.4 30.5
Often 28 13.9 15.0
NA 14 7.0 NA
Total 201 100.0 100.0 
#3
  d3j3 <- as.factor(d[,"d3j3"])
  # Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
  levels(d3j3) <- list(Never="1",
                     Rarely="2",
                     Sometimes="3",
                     Often="4")
  d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
  
  new.d <- data.frame(new.d, d3j3)
  new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
  temp.d <- data.frame (new.d, d3j3)  
  
  result<-questionr::freq(temp.d$d3j3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
n % val%
Never 50 24.9 27.0
Rarely 43 21.4 23.2
Sometimes 63 31.3 34.1
Often 29 14.4 15.7
NA 16 8.0 NA
Total 201 100.0 100.0

D4: How you currently see yourself

  • D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
      1. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
      1. Once you make up your mind to do something, you stay with it until the job is completely done.
      1. You like doing things that other people thought could not be done.
      1. When things don’t go the way you want them to, that just makes you work even harder.
      1. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
      1. It’s not always easy, but you manage to find a way to do the things you really need to get done.
      1. Very seldom have you been disappointed by the results of your hard work.
      1. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
      1. In the past, even when things got really tough, you never lost sight of your goals.
      1. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
      1. You don’t let your personal feelings get in the way of doing a job.
      1. Hard work has really helped you to get ahead in life.
      • 1=Strongly Agree
      • 2=Somewhat Agree
      • 3=Somewhat Disagree
      • 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
  d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
  levels(d4a) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4a)
  new.d <- apply_labels(new.d, d4a = "make life")
  temp.d <- data.frame (new.d, d4a)  
  
  result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
n % val% %cum val%cum
Strongly_Agree 97 48.3 48.7 48.3 48.7
Somewhat_Agree 79 39.3 39.7 87.6 88.4
Somewhat_Disagree 19 9.5 9.5 97.0 98.0
Strongly_Disagree 4 2.0 2.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
  d4b <- as.factor(d[,"d4b"])
  # Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
  levels(d4b) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4b)
  new.d <- apply_labels(new.d, d4b = "until job is done")
  temp.d <- data.frame (new.d, d4b)  
  
  result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
n % val% %cum val%cum
Strongly_Agree 123 61.2 61.8 61.2 61.8
Somewhat_Agree 67 33.3 33.7 94.5 95.5
Somewhat_Disagree 8 4.0 4.0 98.5 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# c. You like doing things that other people thought could not be done.
  d4c <- as.factor(d[,"d4c"])
  # Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
  levels(d4c) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4c)
  new.d <- apply_labels(new.d, d4c = "until job is done")
  temp.d <- data.frame (new.d, d4c)  
  
  result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
n % val% %cum val%cum
Strongly_Agree 97 48.3 48.7 48.3 48.7
Somewhat_Agree 84 41.8 42.2 90.0 91.0
Somewhat_Disagree 18 9.0 9.0 99.0 100.0
Strongly_Disagree 0 0.0 0.0 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# d. When things don’t go the way you want them to, that just makes you work even harder.
  d4d <- as.factor(d[,"d4d"])
  # Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
  levels(d4d) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4d)
  new.d <- apply_labels(new.d, d4d = "until job is done")
  temp.d <- data.frame (new.d, d4d)  
  
  result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
n % val% %cum val%cum
Strongly_Agree 87 43.3 43.7 43.3 43.7
Somewhat_Agree 99 49.3 49.7 92.5 93.5
Somewhat_Disagree 12 6.0 6.0 98.5 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
  d4e <- as.factor(d[,"d4e"])
  # Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
  levels(d4e) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4e)
  new.d <- apply_labels(new.d, d4e = "do it yourself")
  temp.d <- data.frame (new.d, d4e)  
  
  result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
n % val% %cum val%cum
Strongly_Agree 83 41.3 41.5 41.3 41.5
Somewhat_Agree 83 41.3 41.5 82.6 83.0
Somewhat_Disagree 28 13.9 14.0 96.5 97.0
Strongly_Disagree 6 3.0 3.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
  d4f <- as.factor(d[,"d4f"])
  # Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
  levels(d4f) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4f)
  new.d <- apply_labels(new.d, d4f = "not easy but get it done")
  temp.d <- data.frame (new.d, d4f)  
  
  result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
n % val% %cum val%cum
Strongly_Agree 130 64.7 65.7 64.7 65.7
Somewhat_Agree 62 30.8 31.3 95.5 97.0
Somewhat_Disagree 4 2.0 2.0 97.5 99.0
Strongly_Disagree 2 1.0 1.0 98.5 100.0
NA 3 1.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# g. Very seldom have you been disappointed by the results of your hard work.
  d4g <- as.factor(d[,"d4g"])
  # Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
  levels(d4g) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4g)
  new.d <- apply_labels(new.d, d4g = "seldom disappointed")
  temp.d <- data.frame (new.d, d4g)  
  
  result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
n % val% %cum val%cum
Strongly_Agree 67 33.3 34.0 33.3 34.0
Somewhat_Agree 100 49.8 50.8 83.1 84.8
Somewhat_Disagree 24 11.9 12.2 95.0 97.0
Strongly_Disagree 6 3.0 3.0 98.0 100.0
NA 4 2.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
  d4h <- as.factor(d[,"d4h"])
  # Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
  levels(d4h) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4h)
  new.d <- apply_labels(new.d, d4h = "stand up for believes")
  temp.d <- data.frame (new.d, d4h)  
  
  result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
n % val% %cum val%cum
Strongly_Agree 126 62.7 63.3 62.7 63.3
Somewhat_Agree 65 32.3 32.7 95.0 96.0
Somewhat_Disagree 7 3.5 3.5 98.5 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
# i. In the past, even when things got really tough, you never lost sight of your goals.
  d4i <- as.factor(d[,"d4i"])
    # Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
  levels(d4i) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4i)
  new.d <- apply_labels(new.d, d4i = "tough but never lost")
  temp.d <- data.frame (new.d, d4i)  
  
  result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
n % val% %cum val%cum
Strongly_Agree 111 55.2 55.5 55.2 55.5
Somewhat_Agree 76 37.8 38.0 93.0 93.5
Somewhat_Disagree 10 5.0 5.0 98.0 98.5
Strongly_Disagree 3 1.5 1.5 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
  d4j <- as.factor(d[,"d4j"])
    # Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
  levels(d4j) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4j)
  new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
  temp.d <- data.frame (new.d, d4j)  
  
  result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
n % val% %cum val%cum
Strongly_Agree 61 30.3 30.5 30.3 30.5
Somewhat_Agree 80 39.8 40.0 70.1 70.5
Somewhat_Disagree 51 25.4 25.5 95.5 96.0
Strongly_Disagree 8 4.0 4.0 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
#k. You don’t let your personal feelings get in the way of doing a job.
  d4k <- as.factor(d[,"d4k"])
    # Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
  levels(d4k) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4k)
  new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
  temp.d <- data.frame (new.d, d4k)  
  
  result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
n % val% %cum val%cum
Strongly_Agree 99 49.3 49.7 49.3 49.7
Somewhat_Agree 81 40.3 40.7 89.6 90.5
Somewhat_Disagree 18 9.0 9.0 98.5 99.5
Strongly_Disagree 1 0.5 0.5 99.0 100.0
NA 2 1.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
#l. Hard work has really helped you to get ahead in life.
  d4l <- as.factor(d[,"d4l"])
    # Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
  levels(d4l) <- list(Strongly_Agree ="1",
                     Somewhat_Agree="2",
                     Somewhat_Disagree="3",
                     Strongly_Disagree="4")
  d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
  
  new.d <- data.frame(new.d, d4l)
  new.d <- apply_labels(new.d, d4l = "hard work helps")
  temp.d <- data.frame (new.d, d4l)  
  
  result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
n % val% %cum val%cum
Strongly_Agree 126 62.7 63.0 62.7 63.0
Somewhat_Agree 60 29.9 30.0 92.5 93.0
Somewhat_Disagree 13 6.5 6.5 99.0 99.5
Strongly_Disagree 1 0.5 0.5 99.5 100.0
NA 1 0.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

D5: Childhood

  • D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
    1. Did you live with anyone who was depressed, mentally ill, or suicidal?
    1. Did you live with anyone who was a problem drinker or alcoholic?
    1. Did you live with anyone who used illegal street drugs or who abused prescription medications?
    1. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
    1. Were your parents separated or divorced?
    1. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
    1. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
    1. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
    1. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
    1. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
    1. How often did anyone at least 5 years older than you or an adult, force you to have sex?
    • 1=No
    • 2=Yes
    • 3=Parents not married
    • 88=Don’t know/not sure
    • 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
  d5a <- as.factor(d[,"d5a"])
  # Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
  levels(d5a) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5a)
  new.d <- apply_labels(new.d, d5a = "live with depressed")
  temp.d <- data.frame (new.d, d5a)  
  
  result<-questionr::freq(temp.d$d5a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
n % val%
No 158 78.6 78.6
Yes 21 10.4 10.4
Dont_know_not_sure 20 10.0 10.0
Prefer_not_to_answer 2 1.0 1.0
Total 201 100.0 100.0 
# b. Did you live with anyone who was a problem drinker or alcoholic?
  d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
  levels(d5b) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5b)
  new.d <- apply_labels(new.d, d5b = "live with alcoholic")
  temp.d <- data.frame (new.d, d5b)  
  
  result<-questionr::freq(temp.d$d5b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
n % val%
No 132 65.7 65.7
Yes 54 26.9 26.9
Dont_know_not_sure 9 4.5 4.5
Prefer_not_to_answer 6 3.0 3.0
Total 201 100.0 100.0 
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?  
  d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
  levels(d5c) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5c)
  new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
  temp.d <- data.frame (new.d, d5c)  
  
  result<-questionr::freq(temp.d$d5c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
n % val%
No 157 78.1 78.1
Yes 33 16.4 16.4
Dont_know_not_sure 8 4.0 4.0
Prefer_not_to_answer 3 1.5 1.5
Total 201 100.0 100.0 
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility? 
  d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
  levels(d5d) <- list(No="1",
                     Yes="2",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5d)
  new.d <- apply_labels(new.d, d5d = "live with people in a prison")
  temp.d <- data.frame (new.d, d5d)  
  
  result<-questionr::freq(temp.d$d5d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
n % val%
No 167 83.1 83.1
Yes 27 13.4 13.4
Dont_know_not_sure 6 3.0 3.0
Prefer_not_to_answer 1 0.5 0.5
Total 201 100.0 100.0 
# e. Were your parents separated or divorced? 
  d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
  levels(d5e) <- list(No="1",
                     Yes="2",
                     Not_married="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5e)
  new.d <- apply_labels(new.d, d5e = "parents divorced")
  temp.d <- data.frame (new.d, d5e)  
  
  result<-questionr::freq(temp.d$d5e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
n % val%
No 103 51.2 51.2
Yes 75 37.3 37.3
Not_married 16 8.0 8.0
Dont_know_not_sure 2 1.0 1.0
Prefer_not_to_answer 5 2.5 2.5
Total 201 100.0 100.0 
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
  d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
  levels(d5f) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5f)
  new.d <- apply_labels(new.d, d5f = "violence to each other")
  temp.d <- data.frame (new.d, d5f)  
  
  result<-questionr::freq(temp.d$d5f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
n % val%
Never 117 58.2 58.5
Once 19 9.5 9.5
More_than_once 30 14.9 15.0
Dont_know_not_sure 22 10.9 11.0
Prefer_not_to_answer 12 6.0 6.0
NA 1 0.5 NA
Total 201 100.0 100.0 
#  g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
  d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
  levels(d5g) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5g)
  new.d <- apply_labels(new.d, d5g = "violence to you")
  temp.d <- data.frame (new.d, d5g)  
  
  result<-questionr::freq(temp.d$d5g,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
n % val%
Never 146 72.6 73.4
Once 8 4.0 4.0
More_than_once 33 16.4 16.6
Dont_know_not_sure 5 2.5 2.5
Prefer_not_to_answer 7 3.5 3.5
NA 2 1.0 NA
Total 201 100.0 100.0 
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
  d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
  levels(d5h) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5h)
  new.d <- apply_labels(new.d, d5h = "swear insult")
  temp.d <- data.frame (new.d, d5h)  
  
  result<-questionr::freq(temp.d$d5h,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
n % val%
Never 104 51.7 52.0
Once 13 6.5 6.5
More_than_once 60 29.9 30.0
Dont_know_not_sure 13 6.5 6.5
Prefer_not_to_answer 10 5.0 5.0
NA 1 0.5 NA
Total 201 100.0 100.0 
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
  d5i <- as.factor(d[,"d5i"])
  # Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
  levels(d5i) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5i)
  new.d <- apply_labels(new.d, d5i = "touch you sexually")
  temp.d <- data.frame (new.d, d5i)  
  
  result<-questionr::freq(temp.d$d5i,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
n % val%
Never 174 86.6 87.0
Once 11 5.5 5.5
More_than_once 9 4.5 4.5
Dont_know_not_sure 4 2.0 2.0
Prefer_not_to_answer 2 1.0 1.0
NA 1 0.5 NA
Total 201 100.0 100.0 
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
  d5j <- as.factor(d[,"d5j"])
  # Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
  levels(d5j) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5j)
  new.d <- apply_labels(new.d, d5j = "touch them sexually")
  temp.d <- data.frame (new.d, d5j)  
  
  result<-questionr::freq(temp.d$d5j,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
n % val%
Never 179 89.1 90.4
Once 7 3.5 3.5
More_than_once 9 4.5 4.5
Dont_know_not_sure 1 0.5 0.5
Prefer_not_to_answer 2 1.0 1.0
NA 3 1.5 NA
Total 201 100.0 100.0 
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
  d5k <- as.factor(d[,"d5k"])
  # Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
  levels(d5k) <- list(Never="1",
                     Once="2",
                     More_than_once="3",
                     Dont_know_not_sure="88",
                     Prefer_not_to_answer="99")
  d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
  
  new.d <- data.frame(new.d, d5k)
  new.d <- apply_labels(new.d, d5k = "forced to have sex")
  temp.d <- data.frame (new.d, d5k)  
  
  result<-questionr::freq(temp.d$d5k,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
n % val%
Never 186 92.5 93.0
Once 2 1.0 1.0
More_than_once 5 2.5 2.5
Dont_know_not_sure 4 2.0 2.0
Prefer_not_to_answer 3 1.5 1.5
NA 1 0.5 NA
Total 201 100.0 100.0

E1: First indications

  • E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
    • E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
    • E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
    • E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
    • E1_4: 1=I had bone pain that I went to see my doctor about
    • E1_5: 1=I was fearful I had cancer
    • E1_6: 1=Other
# 1
  e1_1 <- as.factor(d[,"e1_1"])
  levels(e1_1) <- list(High_PSA_test="1")

  new.d <- data.frame(new.d, e1_1)
  new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
  temp.d <- data.frame (new.d, e1_1)  
  
  result<-questionr::freq(temp.d$e1_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
n % val%
High_PSA_test 159 79.1 100
NA 42 20.9 NA
Total 201 100.0 100 
#2
  e1_2 <- as.factor(d[,"e1_2"])
  levels(e1_2) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_2)
  new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
  temp.d <- data.frame (new.d, e1_2)  
  
  result<-questionr::freq(temp.d$e1_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
n % val%
Digital_rectal_exam 46 22.9 100
NA 155 77.1 NA
Total 201 100.0 100 
#3
  e1_3 <- as.factor(d[,"e1_3"])
  e1_3[which(e1_3=="*")]<-"NA"
  levels(e1_3) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_3)
  new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
  temp.d <- data.frame (new.d, e1_3)  
  
  result<-questionr::freq(temp.d$e1_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
n % val%
Digital_rectal_exam 38 18.9 100
NA 163 81.1 NA
Total 201 100.0 100 
#4
  e1_4 <- as.factor(d[,"e1_4"])
  e1_4[which(e1_4=="*")]<-"NA"
  levels(e1_4) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_4)
  new.d <- apply_labels(new.d, e1_4 = "bone pain")
  temp.d <- data.frame (new.d, e1_4)  
  
  result<-questionr::freq(temp.d$e1_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
n % val%
Digital_rectal_exam 2 1 100
NA 199 99 NA
Total 201 100 100 
#5
  e1_5 <- as.factor(d[,"e1_5"])
  e1_5[which(e1_5=="*")]<-"NA"
  levels(e1_5) <- list(Digital_rectal_exam="1")

  new.d <- data.frame(new.d, e1_5)
  new.d <- apply_labels(new.d, e1_5 = "fearful")
  temp.d <- data.frame (new.d, e1_5)  
  
  result<-questionr::freq(temp.d$e1_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
n % val%
Digital_rectal_exam 7 3.5 100
NA 194 96.5 NA
Total 201 100.0 100

E1 Other: First indications

e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"

  new.d <- data.frame(new.d, e1other)
  new.d <- apply_labels(new.d, e1other = "e1other")
  temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E1 Other")
E1 Other
n % val%
Back pain, getting married blood test. 1 0.5 6.2
Bleeding in urine after sex. 1 0.5 6.2
Blood in my urine January 2015. 1 0.5 6.2
Blood in urine 2 1.0 12.5
Colon. 1 0.5 6.2
Could not hold urine 1 0.5 6.2
Gut feeling 1 0.5 6.2
Had serious problems urinating. 1 0.5 6.2
I need to be tested. I knew that at age 35 and up. 1 0.5 6.2
I previously applied for insurance policy 1 0.5 6.2
I was having trouble urinating 1 0.5 6.2
My brother had prostate cancer 1 0.5 6.2
My father was diagnosed with prostate cancer. 1 0.5 6.2
Stage 2 radiation it’s negative 1 0.5 6.2
Ultra sound of prostate 1 0.5 6.2
NA 185 92.0 NA
Total 201 100.0 100.0

E2: Before diagnosis

  • E2. Before you were diagnosed with prostate cancer:
      1. Did you have any previous prostate biopsies that were negative?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3 or more
      1. Did you have any previous PSA blood tests that were considered normal?
      • 2=Yes
      • 1=No
      • 88=Don’t know
    • If yes, How many?
      • 1=1
      • 2=2
      • 3=3
      • 4=4
      • 5=5 or more
# 1
  e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
  levels(e2aa) <- list(Yes="2",
                      No="1",
                      Dont_know="88")
  e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2aa)
  new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
  temp.d <- data.frame (new.d, e2aa)  
  
  result<-questionr::freq(temp.d$e2aa,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
n % val%
Yes 24 11.9 12.2
No 155 77.1 78.7
Dont_know 18 9.0 9.1
NA 4 2.0 NA
Total 201 100.0 100.0 
#2
  e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
  levels(e2ab) <- list(One="1",
                      Two="2",
                      Three_more="3")
  e2ab <- ordered(e2ab, c("One","Two","Three_more"))
  
  new.d <- data.frame(new.d, e2ab)
  new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
  temp.d <- data.frame (new.d, e2ab)  
  
  result<-questionr::freq(temp.d$e2ab,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 12 6.0 44.4
Two 10 5.0 37.0
Three_more 5 2.5 18.5
NA 174 86.6 NA
Total 201 100.0 100.0 
#3
  e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
  levels(e2ba) <- list(Yes="2",
                       No="1",
                       Dont_know="88")
  e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
  
  new.d <- data.frame(new.d, e2ba)
  new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
  temp.d <- data.frame (new.d, e2ba)  
  
  result<-questionr::freq(temp.d$e2ba,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
n % val%
Yes 91 45.3 50.6
No 48 23.9 26.7
Dont_know 41 20.4 22.8
NA 21 10.4 NA
Total 201 100.0 100.0 
#4
  e2bb <- as.factor(d[,"e2bb"])
  # Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
  levels(e2bb) <- list(One="1",
                      Two="2",
                      Three="3",
                      Four="4",
                      Five_more="5")
  e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
  
  new.d <- data.frame(new.d, e2bb)
  new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
  temp.d <- data.frame (new.d, e2bb)  
  
  result<-questionr::freq(temp.d$e2bb,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
n % val%
One 11 5.5 13.4
Two 18 9.0 22.0
Threem 0 0.0 0.0
Four 7 3.5 8.5
Five_more 46 22.9 56.1
NA 119 59.2 NA
Total 201 100.0 100.0

E3: Decision about PSA blood test

  • E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4= I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor, nurse, or health care provider made the decision
    • 88=I do not know or remember how the decision was made
  e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
  levels(e3) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e3)
  new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
  temp.d <- data.frame (new.d, e3)  
  
  result<-questionr::freq(temp.d$e3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "E3")
E3
n % val%
Alone 29 14.4 15.2
With_family_or_friends 10 5.0 5.2
With_family_and_doctor 27 13.4 14.1
With_doctor 57 28.4 29.8
Doctor_made 61 30.3 31.9
Dont_know_or_remember 7 3.5 3.7
NA 10 5.0 NA
Total 201 100.0 100.0

E4: Understanding of aggressiveness

  • E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
    • 1=Low risk of progression
    • 2=Intermediate risk of progression
    • 3=High risk of progression
    • 4=Unknown risk of progression
    • 88=Don’t know/Don’t remember
  e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
  levels(e4) <- list(Low_risk="1",
                     Intermediate_risk="2",
                     High_risk="3",
                     Unknown_risk="4",
                     Dont_know_or_remember="88")
  e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e4)
  new.d <- apply_labels(new.d, e4 = "how aggressive")
  temp.d <- data.frame (new.d, e4)  
  
  result<-questionr::freq(temp.d$e4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e4")
e4
n % val%
Low_risk 82 40.8 41.4
Intermediate_risk 42 20.9 21.2
High_risk 41 20.4 20.7
Unknown_risk 14 7.0 7.1
Dont_know_or_remember 19 9.5 9.6
NA 3 1.5 NA
Total 201 100.0 100.0

E5: Gleason score

  • E5. What was your Gleason score when you were diagnosed with prostate cancer?
    • 1=6 or less
    • 2=7
    • 3=8-10
    • 88=Don’t know
  e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
  levels(e5) <- list(Six_less="1",
                     Seven="2",
                     Eight_to_ten="3",
                     Dont_know="88")
  e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
  
  new.d <- data.frame(new.d, e5)
  new.d <- apply_labels(new.d, e5 = "Gleason score")
  temp.d <- data.frame (new.d, e5)  
  
  result<-questionr::freq(temp.d$e5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e5")
e5
n % val%
Six_less 41 20.4 21.1
Seven 37 18.4 19.1
Eight_to_ten 30 14.9 15.5
Dont_know 86 42.8 44.3
NA 7 3.5 NA
Total 201 100.0 100.0

E6: Understanding of stage

  • E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
    • 1=Localized, confined to prostate
    • 2=Regional, tumor extended to regions around the prostate
    • 3=Distant, tumor extended to bones or other parts of body
    • 88=Don’t know about the stage
  e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
  levels(e6) <- list(Localized="1",
                     Regional="2",
                     Distant="3",
                     Dont_know="88")
  e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
  
  new.d <- data.frame(new.d, e6)
  new.d <- apply_labels(new.d, e6 = "Stage")
  temp.d <- data.frame (new.d, e6)  
  
  result<-questionr::freq(temp.d$e6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e6")
e6
n % val%
Localized 152 75.6 77.2
Regional 3 1.5 1.5
Distant 8 4.0 4.1
Dont_know 34 16.9 17.3
NA 4 2.0 NA
Total 201 100.0 100.0

E7: MRI guided biopsy

  • E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
    • 2=Yes
    • 1=No
    • 88=Don’t Know
  e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
  levels(e7) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e7 <- ordered(e7, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e7)
  new.d <- apply_labels(new.d, e7 = "Stage")
  temp.d <- data.frame (new.d, e7)  
  
  result<-questionr::freq(temp.d$e7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e7")
e7
n % val%
No 86 42.8 43.4
Yes 79 39.3 39.9
Dont_know 33 16.4 16.7
NA 3 1.5 NA
Total 201 100.0 100.0

E8: Decision about treatment

  • E8. How did you make your treatment decision?
    • 1=I made the decision alone
    • 2=I made the decision together with a family member or friend
    • 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
    • 4=I made the decision together with my doctor, nurse, or health care provider
    • 5=My doctor , nurse, or health care provider made the decision
    • 6=I don’t know or remember how the decision was made
  e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
  levels(e8) <- list(Alone="1",
                     With_family_or_friends="2",
                     With_family_and_doctor="3",
                     With_doctor="4",
                     Doctor_made="5",
                     Dont_know_or_remember="88")
  e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
  
  new.d <- data.frame(new.d, e8)
  new.d <- apply_labels(new.d, e8 = "treatment decision")
  temp.d <- data.frame (new.d, e8)  
  
  result<-questionr::freq(temp.d$e8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e8")
e8
n % val%
Alone 27 13.4 14.0
With_family_or_friends 28 13.9 14.5
With_family_and_doctor 74 36.8 38.3
With_doctor 48 23.9 24.9
Doctor_made 16 8.0 8.3
Dont_know_or_remember 0 0.0 0.0
NA 8 4.0 NA
Total 201 100.0 100.0

E9: The most important factors of tx

  • E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
    • E9_1: 1=Best chance for cure of my cancer
    • E9_2: 1=Minimize side effects related to sexual function
    • E9_3: 1=Minimize side effects related to urinary function
    • E9_4: 1=Minimize side effects related to bowel function
    • E9_5: 1=Minimize financial cost
    • E9_6: 1=Amount of time and travel required to receive treatments
    • E9_7: 1=Length of recovery time
    • E9_8: 1=Amount of time away from work
    • E9_9: 1=Burden on family members
    • E9_10: 1=Reduce worry and concern about cancer
  e9_1 <- as.factor(d[,"e9_1"])
  levels(e9_1) <- list(Best_for_cure="1")
  new.d <- data.frame(new.d, e9_1)
  new.d <- apply_labels(new.d, e9_1 = "Best for cure")
  temp.d <- data.frame (new.d, e9_1)  
  result<-questionr::freq(temp.d$e9_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
n % val%
Best_for_cure 182 90.5 100
NA 19 9.5 NA
Total 201 100.0 100 
  e9_2 <- as.factor(d[,"e9_2"])
  levels(e9_2) <- list(side_effects_sexual="1")
  new.d <- data.frame(new.d, e9_2)
  new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
  temp.d <- data.frame (new.d, e9_2)  
  result<-questionr::freq(temp.d$e9_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
n % val%
side_effects_sexual 60 29.9 100
NA 141 70.1 NA
Total 201 100.0 100 
  e9_3 <- as.factor(d[,"e9_3"])
  levels(e9_3) <- list(side_effects_urinary="1")
  new.d <- data.frame(new.d, e9_3)
  new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
  temp.d <- data.frame (new.d, e9_3)  
  result<-questionr::freq(temp.d$e9_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
n % val%
side_effects_urinary 56 27.9 100
NA 145 72.1 NA
Total 201 100.0 100 
  e9_4 <- as.factor(d[,"e9_4"])
  levels(e9_4) <- list(side_effects_bowel="1")
  new.d <- data.frame(new.d, e9_4)
  new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
  temp.d <- data.frame (new.d, e9_4)  
  result<-questionr::freq(temp.d$e9_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
n % val%
side_effects_bowel 35 17.4 100
NA 166 82.6 NA
Total 201 100.0 100 
  e9_5 <- as.factor(d[,"e9_5"])
  levels(e9_5) <- list(financial_cost="1")
  new.d <- data.frame(new.d, e9_5)
  new.d <- apply_labels(new.d, e9_5 = "financial cost")
  temp.d <- data.frame (new.d, e9_5)  
  result<-questionr::freq(temp.d$e9_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
n % val%
financial_cost 10 5 100
NA 191 95 NA
Total 201 100 100 
  e9_6 <- as.factor(d[,"e9_6"])
  levels(e9_6) <- list(time_and_travel="1")
  new.d <- data.frame(new.d, e9_6)
  new.d <- apply_labels(new.d, e9_6 = "time and travel")
  temp.d <- data.frame (new.d, e9_6)  
  result<-questionr::freq(temp.d$e9_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
n % val%
time_and_travel 21 10.4 100
NA 180 89.6 NA
Total 201 100.0 100 
  e9_7 <- as.factor(d[,"e9_7"])
  levels(e9_7) <- list(recovery_time="1")
  new.d <- data.frame(new.d, e9_7)
  new.d <- apply_labels(new.d, e9_7 = "recovery time")
  temp.d <- data.frame (new.d, e9_7)  
  result<-questionr::freq(temp.d$e9_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
n % val%
recovery_time 40 19.9 100
NA 161 80.1 NA
Total 201 100.0 100 
  e9_8 <- as.factor(d[,"e9_8"])
  levels(e9_8) <- list(time_away_from_work="1")
  new.d <- data.frame(new.d, e9_8)
  new.d <- apply_labels(new.d, e9_8 = "time away from work")
  temp.d <- data.frame (new.d, e9_8)  
  result<-questionr::freq(temp.d$e9_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
n % val%
time_away_from_work 20 10 100
NA 181 90 NA
Total 201 100 100 
  e9_9 <- as.factor(d[,"e9_9"])
  levels(e9_9) <- list(family_burden="1")
  new.d <- data.frame(new.d, e9_9)
  new.d <- apply_labels(new.d, e9_9 = "family burden")
  temp.d <- data.frame (new.d, e9_9)  
  result<-questionr::freq(temp.d$e9_9,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
n % val%
family_burden 28 13.9 100
NA 173 86.1 NA
Total 201 100.0 100 
  e9_10 <- as.factor(d[,"e9_10"])
  levels(e9_10) <- list(Reduce_worry_concern="1")
  new.d <- data.frame(new.d, e9_10)
  new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
  temp.d <- data.frame (new.d, e9_10)  
  result<-questionr::freq(temp.d$e9_10,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
n % val%
Reduce_worry_concern 81 40.3 100
NA 120 59.7 NA
Total 201 100.0 100

E10: Recieved treatment

  • E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
    • E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
    • E10_2: 1=Active Surveillance or watchful waiting
    • E10_3: 1=Prostate surgery (prostatectomy)
    • E10_4: 1=Radiation to the prostate
    • E10_5: 1=Hormonal treatments
    • E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
    • E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
    • E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
  e10_1 <- as.factor(d[,"e10_1"])
  levels(e10_1) <- list(no_treatment="1")
  new.d <- data.frame(new.d, e10_1)
  new.d <- apply_labels(new.d, e10_1 = "no treatment")
  temp.d <- data.frame (new.d, e10_1)  
  result<-questionr::freq(temp.d$e10_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment  yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
n % val%
no_treatment 12 6 100
NA 189 94 NA
Total 201 100 100 
  e10_2 <- as.factor(d[,"e10_2"])
  levels(e10_2) <- list(Active_Surveillance="1")
  new.d <- data.frame(new.d, e10_2)
  new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
  temp.d <- data.frame (new.d, e10_2)  
  result<-questionr::freq(temp.d$e10_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
n % val%
Active_Surveillance 42 20.9 100
NA 159 79.1 NA
Total 201 100.0 100 
  e10_3 <- as.factor(d[,"e10_3"])
  levels(e10_3) <- list(prostatectomy="1")
  new.d <- data.frame(new.d, e10_3)
  new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
  temp.d <- data.frame (new.d, e10_3)  
  result<-questionr::freq(temp.d$e10_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
n % val%
prostatectomy 45 22.4 100
NA 156 77.6 NA
Total 201 100.0 100 
  e10_4 <- as.factor(d[,"e10_4"])
  levels(e10_4) <- list(Radiation="1")
  new.d <- data.frame(new.d, e10_4)
  new.d <- apply_labels(new.d, e10_4 = "Radiation")
  temp.d <- data.frame (new.d, e10_4)  
  result<-questionr::freq(temp.d$e10_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
n % val%
Radiation 77 38.3 100
NA 124 61.7 NA
Total 201 100.0 100 
  e10_5 <- as.factor(d[,"e10_5"])
  levels(e10_5) <- list(Hormonal_treatments="1")
  new.d <- data.frame(new.d, e10_5)
  new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
  temp.d <- data.frame (new.d, e10_5)  
  result<-questionr::freq(temp.d$e10_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
n % val%
Hormonal_treatments 26 12.9 100
NA 175 87.1 NA
Total 201 100.0 100 
  e10_6 <- as.factor(d[,"e10_6"])
  levels(e10_6) <- list(Provenge_immunotherapy="1")
  new.d <- data.frame(new.d, e10_6)
  new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
  temp.d <- data.frame (new.d, e10_6)  
  result<-questionr::freq(temp.d$e10_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
n % val%
Provenge_immunotherapy 3 1.5 100
NA 198 98.5 NA
Total 201 100.0 100 
  e10_7 <- as.factor(d[,"e10_7"])
  levels(e10_7) <- list(Chemotherapy="1")
  new.d <- data.frame(new.d, e10_7)
  new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
  temp.d <- data.frame (new.d, e10_7)  
  result<-questionr::freq(temp.d$e10_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
n % val%
Chemotherapy 6 3 100
NA 195 97 NA
Total 201 100 100 
  e10_8 <- as.factor(d[,"e10_8"])
  levels(e10_8) <- list(Other="1")
  new.d <- data.frame(new.d, e10_8)
  new.d <- apply_labels(new.d, e10_8 = "Other")
  temp.d <- data.frame (new.d, e10_8)  
  result<-questionr::freq(temp.d$e10_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
n % val%
Other 12 6 100
NA 189 94 NA
Total 201 100 100

E10-3 Prostatectomy

  • E10_3. Prostate surgery (prostatectomy), indicate which type(s):
    • E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
    • E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
    • E10_3_3: 1=Had surgery but unsure of type
  e10_3_1 <- as.factor(d[,"e10_3_1"])
  levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
  new.d <- data.frame(new.d, e10_3_1)
  new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
  temp.d <- data.frame (new.d, e10_3_1)  
  result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
n % val%
Robotic_laproscopic_surgery 43 21.4 100
NA 158 78.6 NA
Total 201 100.0 100 
  e10_3_2 <- as.factor(d[,"e10_3_2"])
  levels(e10_3_2) <- list(Open_surgical_removal="1")
  new.d <- data.frame(new.d, e10_3_2)
  new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
  temp.d <- data.frame (new.d, e10_3_2)  
  result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
n % val%
Open_surgical_removal 10 5 100
NA 191 95 NA
Total 201 100 100 
  e10_3_3 <- as.factor(d[,"e10_3_3"])
  levels(e10_3_3) <- list(unsure_of_type="1")
  new.d <- data.frame(new.d, e10_3_3)
  new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
  temp.d <- data.frame (new.d, e10_3_3)  
  result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
n % val%
unsure_of_type 9 4.5 100
NA 192 95.5 NA
Total 201 100.0 100

E10-4 Radiation

  • E10_4. Radiation to the prostate, indicate which type(s):
    • E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
    • E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
    • E10_4_3: 1=Other types of radiation therapy, or unsure of what type
  e10_4_1 <- as.factor(d[,"e10_4_1"])
  levels(e10_4_1) <- list(External_beam_radiation="1")
  new.d <- data.frame(new.d, e10_4_1)
  new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
  temp.d <- data.frame (new.d, e10_4_1)  
  result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
n % val%
External_beam_radiation 77 38.3 100
NA 124 61.7 NA
Total 201 100.0 100 
  e10_4_2 <- as.factor(d[,"e10_4_2"])
  levels(e10_4_2) <- list(brachytherapy="1")
  new.d <- data.frame(new.d, e10_4_2)
  new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
  temp.d <- data.frame (new.d, e10_4_2)  
  result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
n % val%
brachytherapy 41 20.4 100
NA 160 79.6 NA
Total 201 100.0 100 
  e10_4_3 <- as.factor(d[,"e10_4_3"])
  levels(e10_4_3) <- list(Other_types="1")
  new.d <- data.frame(new.d, e10_4_3)
  new.d <- apply_labels(new.d, e10_4_3 = "Other types")
  temp.d <- data.frame (new.d, e10_4_3)  
  result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
n % val%
Other_types 13 6.5 100
NA 188 93.5 NA
Total 201 100.0 100

E10-5 Hormonal treatments

  • E10_5. Hormonal treatments, indicate which type(s):
    • E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
    • E10_5_2: 1= Surgical removal of testicles (orchiectomy)
    • E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
    • E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
    • E10_5_5: 1=Had hormone treatment, but unsure of type
  e10_5_1 <- as.factor(d[,"e10_5_1"])
  levels(e10_5_1) <- list(Hormone_shots="1")
  new.d <- data.frame(new.d, e10_5_1)
  new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
  temp.d <- data.frame (new.d, e10_5_1)  
  result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
n % val%
Hormone_shots 53 26.4 100
NA 148 73.6 NA
Total 201 100.0 100 
  e10_5_2 <- as.factor(d[,"e10_5_2"])
  levels(e10_5_2) <- list(orchiectomy="1")
  new.d <- data.frame(new.d, e10_5_2)
  new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
  temp.d <- data.frame (new.d, e10_5_2)  
  result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
n % val%
orchiectomy 0 0 NaN
NA 201 100 NA
Total 201 100 100 
  e10_5_3 <- as.factor(d[,"e10_5_3"])
  levels(e10_5_3) <- list(Casodex_Eulexin="1")
  new.d <- data.frame(new.d, e10_5_3)
  new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
  temp.d <- data.frame (new.d, e10_5_3)  
  result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
n % val%
Casodex_Eulexin 2 1 100
NA 199 99 NA
Total 201 100 100 
  e10_5_4 <- as.factor(d[,"e10_5_4"])
  levels(e10_5_4) <- list(Zytiga_Xtandi="1")
  new.d <- data.frame(new.d, e10_5_4)
  new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
  temp.d <- data.frame (new.d, e10_5_4)  
  result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
n % val%
Zytiga_Xtandi 3 1.5 100
NA 198 98.5 NA
Total 201 100.0 100 
  e10_5_5 <- as.factor(d[,"e10_5_5"])
  levels(e10_5_5) <- list(unsure_type="1")
  new.d <- data.frame(new.d, e10_5_5)
  new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
  temp.d <- data.frame (new.d, e10_5_5)  
  result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
n % val%
unsure_type 8 4 100
NA 193 96 NA
Total 201 100 100

E11: Treatment decision

  • E11. Your treatment decision: How true is each of the following statements for you?
      1. I had all the information I needed when a treatment was chosen for my prostate cancer
      1. My doctors told me the whole story about the effects of treatment
      1. I knew the right questions to ask my doctor
      1. I had enough time to make a decision about my treatment
      1. I am satisfied with the choices I made in treating my prostate cancer
      1. I would recommend the treatment I had to a close relative or friend
      • 1=Not at all
      • 2=A little bit
      • 3=Somewhat
      • 4=Quite a bit
      • 5=Very much
  e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
  levels(e11a) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11a)
  new.d <- apply_labels(new.d, e11a = "all info")
  temp.d <- data.frame (new.d, e11a)  
  result<-questionr::freq(temp.d$e11a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
n % val%
Not_at_all 5 2.5 2.6
A_little_bit 3 1.5 1.5
Somewhat 26 12.9 13.3
Quite_a_bit 53 26.4 27.2
Very_much 108 53.7 55.4
NA 6 3.0 NA
Total 201 100.0 100.0 
  e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
  levels(e11b) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11b)
  new.d <- apply_labels(new.d, e11b = "be told about effects")
  temp.d <- data.frame (new.d, e11b)  
  result<-questionr::freq(temp.d$e11b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
n % val%
Not_at_all 6 3.0 3.1
A_little_bit 4 2.0 2.1
Somewhat 24 11.9 12.3
Quite_a_bit 48 23.9 24.6
Very_much 113 56.2 57.9
NA 6 3.0 NA
Total 201 100.0 100.0 
  e11c <- as.factor(d[,"e11c"])
  # Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
  levels(e11c) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11c)
  new.d <- apply_labels(new.d, e11c = "right questions to ask")
  temp.d <- data.frame (new.d, e11c)  
  result<-questionr::freq(temp.d$e11c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
n % val%
Not_at_all 14 7.0 7.3
A_little_bit 23 11.4 11.9
Somewhat 74 36.8 38.3
Quite_a_bit 37 18.4 19.2
Very_much 45 22.4 23.3
NA 8 4.0 NA
Total 201 100.0 100.0 
  e11d <- as.factor(d[,"e11d"])
  # Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
  levels(e11d) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11d)
  new.d <- apply_labels(new.d, e11d = "enough time to decide")
  temp.d <- data.frame (new.d, e11d)  
  result<-questionr::freq(temp.d$e11d,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
n % val%
Not_at_all 2 1.0 1.0
A_little_bit 6 3.0 3.1
Somewhat 31 15.4 16.0
Quite_a_bit 48 23.9 24.7
Very_much 107 53.2 55.2
NA 7 3.5 NA
Total 201 100.0 100.0 
  e11e <- as.factor(d[,"e11e"])
  # Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
  levels(e11e) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11e)
  new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
  temp.d <- data.frame (new.d, e11e)  
  result<-questionr::freq(temp.d$e11e,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
n % val%
Not_at_all 5 2.5 2.6
A_little_bit 10 5.0 5.1
Somewhat 29 14.4 14.9
Quite_a_bit 20 10.0 10.3
Very_much 131 65.2 67.2
NA 6 3.0 NA
Total 201 100.0 100.0 
  e11f <- as.factor(d[,"e11f"])
  # Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
  levels(e11f) <- list(Not_at_all="1",
                       A_little_bit="2",
                       Somewhat="3",
                       Quite_a_bit="4",
                       Very_much="5")
  new.d <- data.frame(new.d, e11f)
  new.d <- apply_labels(new.d, e11f = "would recommend")
  temp.d <- data.frame (new.d, e11f)  
  result<-questionr::freq(temp.d$e11f,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
n % val%
Not_at_all 6 3.0 3.1
A_little_bit 8 4.0 4.1
Somewhat 35 17.4 18.1
Quite_a_bit 20 10.0 10.4
Very_much 124 61.7 64.2
NA 8 4.0 NA
Total 201 100.0 100.0

E12: Instructions from doctors or nurses

  • E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
  levels(e12) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e12 <- ordered(e12, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e12)
  new.d <- apply_labels(new.d, e12 = "received instructions")
  temp.d <- data.frame (new.d, e12)  
  
  result<-questionr::freq(temp.d$e12,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e12")
e12
n % val%
No 20 10.0 10.2
Yes 169 84.1 85.8
Dont_know 8 4.0 4.1
NA 4 2.0 NA
Total 201 100.0 100.0

E13: # of PSA blood test

  • E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
    • 0=None
    • 1=1
    • 2=2
    • 3=3
    • 4=4 or more
    • 88=Don’t know/not sure
  e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
  levels(e13) <- list(None="0",
                      One="1",
                      Two="2",
                     Three="3",
                     Four_more="4",
                     Dont_know="88")
  e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
  
  new.d <- data.frame(new.d, e13)
  new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
  temp.d <- data.frame (new.d, e13)  
  
  result<-questionr::freq(temp.d$e13,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e13")
e13
n % val%
None 3 1.5 1.5
One 1 0.5 0.5
Two 8 4.0 4.1
Three 25 12.4 12.9
Four_more 142 70.6 73.2
Dont_know 15 7.5 7.7
NA 7 3.5 NA
Total 201 100.0 100.0

E14: Be told PSA was rising

  • E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
  levels(e14) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e14 <- ordered(e14, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e14)
  new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
  temp.d <- data.frame (new.d, e14)  
  
  result<-questionr::freq(temp.d$e14,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e14")
e14
n % val%
No 141 70.1 71.9
Yes 45 22.4 23.0
Dont_know 10 5.0 5.1
NA 5 2.5 NA
Total 201 100.0 100.0

E15: Recurred or got worse

  • E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
    • 2=Yes
    • 1=No
    • 88=Don’t Know/not sure
  e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
  levels(e15) <- list(No="1",
                     Yes="2",
                     Dont_know="88")
  e15 <- ordered(e15, c("No","Yes","Dont_know"))
  
  new.d <- data.frame(new.d, e15)
  new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
  temp.d <- data.frame (new.d, e15)  
  
  result<-questionr::freq(temp.d$e15,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "e15")
e15
n % val%
No 172 85.6 88.2
Yes 14 7.0 7.2
Dont_know 9 4.5 4.6
NA 6 3.0 NA
Total 201 100.0 100.0

F1: Height

  • F1. How tall are you?
  f1cm <- d[,"f1cm"]
 
  new.d <- data.frame(new.d, f1cm)
  new.d <- apply_labels(new.d, f1cm = "height in cm")
  temp.d <- data.frame (new.d, f1cm)  
  
  result<-questionr::freq(temp.d$f1cm,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
n % val%
8 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100

F2: Weight

  • F2. How much do you current weight?
  f2lbs <- d[,"f2lbs"]
  new.d <- data.frame(new.d, f2lbs)
  new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
  temp.d <- data.frame (new.d, f2lbs)  
  result<-questionr::freq(temp.d$f2lbs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
n % val%
* 1 0.5 0.6
1 1 0.5 0.6
127 2 1.0 1.1
14* 1 0.5 0.6
140 2 1.0 1.1
145 1 0.5 0.6
150 4 2.0 2.3
154 2 1.0 1.1
160 3 1.5 1.7
161 1 0.5 0.6
163 2 1.0 1.1
164 1 0.5 0.6
165 3 1.5 1.7
167 1 0.5 0.6
168 1 0.5 0.6
169 1 0.5 0.6
170 3 1.5 1.7
171 1 0.5 0.6
172 1 0.5 0.6
173 1 0.5 0.6
174 1 0.5 0.6
175 3 1.5 1.7
177 1 0.5 0.6
178 1 0.5 0.6
180 6 3.0 3.4
182 1 0.5 0.6
183 1 0.5 0.6
184 1 0.5 0.6
185 2 1.0 1.1
186 1 0.5 0.6
187 1 0.5 0.6
188 4 2.0 2.3
189 2 1.0 1.1
190 6 3.0 3.4
192 1 0.5 0.6
193 1 0.5 0.6
195 9 4.5 5.1
196 3 1.5 1.7
197 3 1.5 1.7
198 2 1.0 1.1
199 1 0.5 0.6
2* 2 1.0 1.1
200 6 3.0 3.4
203 1 0.5 0.6
204 1 0.5 0.6
205 2 1.0 1.1
210 3 1.5 1.7
212 1 0.5 0.6
214 3 1.5 1.7
215 1 0.5 0.6
216 1 0.5 0.6
218 1 0.5 0.6
220 10 5.0 5.7
221 1 0.5 0.6
224 3 1.5 1.7
225 4 2.0 2.3
226 2 1.0 1.1
228 1 0.5 0.6
229 2 1.0 1.1
23 1 0.5 0.6
230 4 2.0 2.3
233 1 0.5 0.6
235 2 1.0 1.1
237 2 1.0 1.1
240 3 1.5 1.7
245 1 0.5 0.6
247 1 0.5 0.6
249 1 0.5 0.6
250 2 1.0 1.1
254 1 0.5 0.6
255 2 1.0 1.1
260 4 2.0 2.3
262 1 0.5 0.6
265 1 0.5 0.6
266 1 0.5 0.6
270 4 2.0 2.3
272 1 0.5 0.6
280 1 0.5 0.6
285 1 0.5 0.6
292 1 0.5 0.6
310 1 0.5 0.6
311 1 0.5 0.6
315 1 0.5 0.6
330 1 0.5 0.6
340 1 0.5 0.6
416 1 0.5 0.6
53 1 0.5 0.6
65 1 0.5 0.6
75 1 0.5 0.6
90 1 0.5 0.6
NA 26 12.9 NA
Total 201 100.0 100.0 
  f2kgs <- d[,"f2kgs"]
  new.d <- data.frame(new.d, f2kgs)
  new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
  temp.d <- data.frame (new.d, f2kgs)  
  result<-questionr::freq(temp.d$f2kgs,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
n % val%
8 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100

F3: Exercise frequency

  • F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
    • 4=5-7 times per week
    • 3=3-4 times per week
    • 2=1-2 times per week
    • 1=Less than once per week/do not exercise
  f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
  levels(f3) <- list(Per_week_5_7="4",
                     Per_week_3_4="3",
                     Per_week_1_2="2",
                     Per_week_less_1="1")
  f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
  
  new.d <- data.frame(new.d, f3)
  new.d <- apply_labels(new.d, f3 = "exercise")
  temp.d <- data.frame (new.d, f3)  
  
  result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
n % val% %cum val%cum
Per_week_5_7 36 17.9 19.4 17.9 19.4
Per_week_3_4 68 33.8 36.6 51.7 55.9
Per_week_1_2 50 24.9 26.9 76.6 82.8
Per_week_less_1 32 15.9 17.2 92.5 100.0
NA 15 7.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

F4: Minutes of exercise

  • F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
    • 2=Less than 30 minutes
    • 3=30 minutes – 1 hour
    • 4=More than 1 hour
    • 1=Do not exercise
  f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
  levels(f4) <- list(Less_than_30_min="2",
                     Between_30_min_1_hour="3",
                     More_than_1_hour="4",
                     Do_not_exercise="1")
  f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
  
  new.d <- data.frame(new.d, f4)
  new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
  temp.d <- data.frame (new.d, f4)  
  
  result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F4")
F4
n % val% %cum val%cum
Less_than_30_min 31 15.4 16.8 15.4 16.8
Between_30_min_1_hour 83 41.3 44.9 56.7 61.6
More_than_1_hour 57 28.4 30.8 85.1 92.4
Do_not_exercise 14 7.0 7.6 92.0 100.0
NA 16 8.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

F5: Drink alcohol frequency

  • F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
    • 6=Everyday
    • 5=5-6 times per week
    • 4=3-4 times per week
    • 3=1-2 times per week
    • 2=Fewer than once per week
    • 1=Did not drink
  f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
  levels(f5) <- list(Everyday="6",
                     Per_week_5_6_times="5",
                     Per_week_3_4_times="4",
                     Per_week_1_2_times="3",
                     Per_week_fewer_once="2",
                     Not_drink="1")
  f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
  
  new.d <- data.frame(new.d, f5)
  new.d <- apply_labels(new.d, f5 = "how often drink")
  temp.d <- data.frame (new.d, f5)  
  
  result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f5")
f5
n % val% %cum val%cum
Everyday 14 7.0 7.1 7.0 7.1
Per_week_5_6_times 14 7.0 7.1 13.9 14.3
Per_week_3_4_times 19 9.5 9.7 23.4 24.0
Per_week_1_2_times 40 19.9 20.4 43.3 44.4
Per_week_fewer_once 33 16.4 16.8 59.7 61.2
Not_drink 76 37.8 38.8 97.5 100.0
NA 5 2.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

F6: How many drinks

  • F6. When you drank during the past month, how many drinks do you have on a typical occasion?
    • 3=3 or more drinks
    • 2=1-2 drinks
    • 1=Did not drink
  f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
  levels(f6) <- list(Three_or_more="3",
                     One_to_two_drinks="2",
                     Not_drink="1")
  f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
  
  new.d <- data.frame(new.d, f6)
  new.d <- apply_labels(new.d, f6 = "how many drinks")
  temp.d <- data.frame (new.d, f6)  
  
  result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "f6")
f6
n % val% %cum val%cum
Three_or_more 15 7.5 7.6 7.5 7.6
One_to_two_drinks 103 51.2 52.3 58.7 59.9
Not_drink 79 39.3 40.1 98.0 100.0
NA 4 2.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

F7: Smoking history

  • F7. Have you ever smoked at least 100 cigarettes in your lifetime?
    • 1=No
    • 2=Yes
  • F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
    • 555 = “Less than 10”
    • 777 = “75+”
  • F7a. How many cigarettes do you (or did you) usually smoke per day?
    • 1=1-5
    • 2=6-10
    • 3=11-20
    • 4=21-30
    • 5=31+
  • F7b. Have you quit smoking?
    • 1=No
    • 2=Yes
  • F7BAge. If yes, At what age did you quit?
    • 555 = “Less than 10”
    • 777 = “75+”
  f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
  levels(f7) <- list(Yes="2",
                     No="1")
  f7 <- ordered(f7, c("No","Yes"))
  
  new.d <- data.frame(new.d, f7)
  new.d <- apply_labels(new.d, f7 = "smoke")
  temp.d <- data.frame (new.d, f7)  
  
  result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
n % val% %cum val%cum
No 95 47.3 50.3 47.3 50.3
Yes 94 46.8 49.7 94.0 100.0
NA 12 6.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  f7age <- d[,"f7age"]
  f7age[which(f7age=="555")]<-"Less_than_10"
  f7age[which(f7age=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7age)
  new.d <- apply_labels(new.d, f7age = "age start to smoke")
  temp.d <- data.frame (new.d, f7age)  
  
  result<-questionr::freq(temp.d$f7age,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
n % val%
0 1 0.5 1.3
11 1 0.5 1.3
12 2 1.0 2.6
13 5 2.5 6.6
14 3 1.5 3.9
15 1 0.5 1.3
16 10 5.0 13.2
17 4 2.0 5.3
18 9 4.5 11.8
19 12 6.0 15.8
20 5 2.5 6.6
21 3 1.5 3.9
22 4 2.0 5.3
23 2 1.0 2.6
24 4 2.0 5.3
25 2 1.0 2.6
26 1 0.5 1.3
30 1 0.5 1.3
34 1 0.5 1.3
35 1 0.5 1.3
40 1 0.5 1.3
45 1 0.5 1.3
47 1 0.5 1.3
59 1 0.5 1.3
NA 125 62.2 NA
Total 201 100.0 100.0 
  f7a <- as.factor(d[,"f7a"])
  # Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
  levels(f7a) <- list(One_to_five="1",
                     Six_to_ten="2",
                     Eleven_to_twenty="3",
                     Twentyone_to_Thirty="4",
                     Older_31="5")
  f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))

  new.d <- data.frame(new.d, f7a)
  new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
  temp.d <- data.frame (new.d, f7a)  
  
  result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
n % val% %cum val%cum
One_to_five 40 19.9 41.2 19.9 41.2
Six_to_ten 29 14.4 29.9 34.3 71.1
Eleven_to_twenty 21 10.4 21.6 44.8 92.8
Twentyone_to_Thirty 6 3.0 6.2 47.8 99.0
Older_31 1 0.5 1.0 48.3 100.0
NA 104 51.7 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0 
  f7b <- as.factor(d[,"f7b"])
    # Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
  levels(f7b) <- list(No="1",
                     Yes="2")

  new.d <- data.frame(new.d, f7b)
  new.d <- apply_labels(new.d, f7b = "quit smoking")
  temp.d <- data.frame (new.d, f7b)  
  
  result<-questionr::freq(temp.d$f7b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
n % val%
No 16 8.0 17.2
Yes 77 38.3 82.8
NA 108 53.7 NA
Total 201 100.0 100.0 
  f7bage <- d[,"f7bage"]
  f7bage[which(f7bage=="555")]<-"Less_than_10"
  f7bage[which(f7bage=="777")]<-"More_than_75"

  new.d <- data.frame(new.d, f7bage)
  new.d <- apply_labels(new.d, f7bage = "age quit smoking")
  temp.d <- data.frame (new.d, f7bage)  
  
  result<-questionr::freq(temp.d$f7bage,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
n % val%
16 1 0.5 1.3
20 2 1.0 2.6
21 2 1.0 2.6
24 2 1.0 2.6
25 2 1.0 2.6
26 1 0.5 1.3
27 2 1.0 2.6
28 5 2.5 6.5
30 4 2.0 5.2
31 3 1.5 3.9
32 1 0.5 1.3
33 1 0.5 1.3
34 1 0.5 1.3
35 4 2.0 5.2
36 3 1.5 3.9
37 2 1.0 2.6
38 3 1.5 3.9
40 4 2.0 5.2
42 1 0.5 1.3
43 2 1.0 2.6
45 5 2.5 6.5
46 1 0.5 1.3
47 1 0.5 1.3
49 2 1.0 2.6
50 3 1.5 3.9
51 1 0.5 1.3
52 2 1.0 2.6
55 2 1.0 2.6
56 1 0.5 1.3
58 1 0.5 1.3
59 2 1.0 2.6
60 1 0.5 1.3
62 1 0.5 1.3
63 2 1.0 2.6
64 1 0.5 1.3
65 2 1.0 2.6
66 2 1.0 2.6
70 1 0.5 1.3
NA 124 61.7 NA
Total 201 100.0 100.0

G1: Marital status

  • G1. What is your current marital status?
    • 1=Married, or living with a partner
    • 2=Separated
    • 3=Divorced
    • 4=Widowed
    • 5=Never Married
  g1 <- as.factor(d[,"g1"])
  # Make "*" to NA
g1[which(g1=="*")]<-"NA"
  levels(g1) <- list(Married_partner="1",
                     Separated="2",
                     Divorced="3",
                     Widowed="4",
                     Never_Married="5")
  g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
  
  new.d <- data.frame(new.d, g1)
  new.d <- apply_labels(new.d, g1 = "marital status")
  temp.d <- data.frame (new.d, g1)  
  
  result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "g1")
g1
n % val% %cum val%cum
Married_partner 114 56.7 57.6 56.7 57.6
Separated 9 4.5 4.5 61.2 62.1
Divorced 35 17.4 17.7 78.6 79.8
Widowed 13 6.5 6.6 85.1 86.4
Never_Married 27 13.4 13.6 98.5 100.0
NA 3 1.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

G2: With whom do you live

  • G2. With whom do you live? Mark all that apply.
    • G2_1: 1=Live alone
    • G2_2: 1=A spouse or partner
    • G2_3: 1=Other family
    • G2_4: 1=Other people (non-family)
    • G2_5: 1=Pets
  g2_1 <- as.factor(d[,"g2_1"])
  levels(g2_1) <- list(Live_alone="1")

  new.d <- data.frame(new.d, g2_1)
  new.d <- apply_labels(new.d, g2_1 = "Live alone")
  temp.d <- data.frame (new.d, g2_1)  
  
  result<-questionr::freq(temp.d$g2_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
n % val%
Live_alone 47 23.4 100
NA 154 76.6 NA
Total 201 100.0 100 
  g2_2 <- as.factor(d[,"g2_2"])
  levels(g2_2) <- list(spouse_partner="1")

  new.d <- data.frame(new.d, g2_2)
  new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
  temp.d <- data.frame (new.d, g2_2)  
  
  result<-questionr::freq(temp.d$g2_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
n % val%
spouse_partner 117 58.2 100
NA 84 41.8 NA
Total 201 100.0 100 
  g2_3 <- as.factor(d[,"g2_3"])
  levels(g2_3) <- list(Other_family="1")

  new.d <- data.frame(new.d, g2_3)
  new.d <- apply_labels(new.d, g2_3 = "Other family")
  temp.d <- data.frame (new.d, g2_3)  
  
  result<-questionr::freq(temp.d$g2_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
n % val%
Other_family 40 19.9 100
NA 161 80.1 NA
Total 201 100.0 100 
  g2_4 <- as.factor(d[,"g2_4"])
  levels(g2_4) <- list(Other_non_family="1")

  new.d <- data.frame(new.d, g2_4)
  new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
  temp.d <- data.frame (new.d, g2_4)  
  
  result<-questionr::freq(temp.d$g2_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
n % val%
Other_non_family 10 5 100
NA 191 95 NA
Total 201 100 100 
  g2_5 <- as.factor(d[,"g2_5"])
  levels(g2_5) <- list(Pets="1")

  new.d <- data.frame(new.d, g2_5)
  new.d <- apply_labels(new.d, g2_5 = "Pets")
  temp.d <- data.frame (new.d, g2_5)  
  
  result<-questionr::freq(temp.d$g2_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
n % val%
Pets 16 8 100
NA 185 92 NA
Total 201 100 100

G3: Identify yourself

  • G3. How do you identify yourself?
    • 1=Straight/heterosexual
    • 2=Bisexual
    • 3=Gay/homosexual/same gender loving
    • 4=Other
    • 99=Prefer not to answer
  g3 <- as.factor(d[,"g3"])
  # Make "*" to NA
g3[which(g3=="*")]<-"NA"
  levels(g3) <- list(heterosexual="1",
                      Bisexual="2",
                       homosexual="3",
                       Other="4",
                       Prefer_not_to_answer="99")
  g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))

  new.d <- data.frame(new.d, g3)
  new.d <- apply_labels(new.d, g3 = "identify yourself")
  temp.d <- data.frame (new.d, g3)  
  
  result<-questionr::freq(temp.d$g3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g3")
g3
n % val%
heterosexual 176 87.6 89.3
Bisexual 1 0.5 0.5
homosexual 16 8.0 8.1
Other 1 0.5 0.5
Prefer_not_to_answer 3 1.5 1.5
NA 4 2.0 NA
Total 201 100.0 100.0

G3 Other: Identify yourself

g3other <- d[,"g3other"]
  new.d <- data.frame(new.d, g3other)
  new.d <- apply_labels(new.d, g3other = "g3other")
  temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G3 Other")
G3 Other
n % val%
Human 1 0.5 25
I am straight. 2 1.0 50
Straight male. 1 0.5 25
NA 197 98.0 NA
Total 201 100.0 100

G4: Education

  • G4. What is the HIGHEST level of education you, your father, and your mother have completed?
    • 1=Grade school or less
    • 2=Some high school
    • 3=High school graduate or GED
    • 4=Vocational school
    • 5=Some college
    • 6=Associate’s degree
    • 7=College graduate (Bachelor’s degree)
    • 8=Some graduate education
    • 9=Graduate degree
    • 88=Don’t know
  g4a <- as.factor(d[,"g4a"])
  # Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
  levels(g4a) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9")
  g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))

  new.d <- data.frame(new.d, g4a)
  new.d <- apply_labels(new.d, g4a = "education")
  temp.d <- data.frame (new.d, g4a)  
  
  result<-questionr::freq(temp.d$g4a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
n % val%
Grade_school_or_less 4 2.0 2.2
Some_high_school 7 3.5 3.9
High_school_graduate_GED 31 15.4 17.2
Vocational_school 6 3.0 3.3
Some_college 48 23.9 26.7
Associate_degree 21 10.4 11.7
College_graduate 26 12.9 14.4
Some_graduate_education 6 3.0 3.3
Graduate_degree 31 15.4 17.2
NA 21 10.4 NA
Total 201 100.0 100.0 
  g4b <- as.factor(d[,"g4b"])
    # Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
  levels(g4b) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4b)
  new.d <- apply_labels(new.d, g4b = "education-father")
  temp.d <- data.frame (new.d, g4b)  
  
  result<-questionr::freq(temp.d$g4b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
n % val%
Grade_school_or_less 27 13.4 16.1
Some_high_school 31 15.4 18.5
High_school_graduate_GED 36 17.9 21.4
Vocational_school 6 3.0 3.6
Some_college 12 6.0 7.1
Associate_degree 3 1.5 1.8
College_graduate 13 6.5 7.7
Some_graduate_education 0 0.0 0.0
Graduate_degree 7 3.5 4.2
Dont_know 33 16.4 19.6
NA 33 16.4 NA
Total 201 100.0 100.0 
  g4c <- as.factor(d[,"g4c"])
    # Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
  levels(g4c) <- list(Grade_school_or_less="1",
                      Some_high_school="2",
                       High_school_graduate_GED="3",
                       Vocational_school="4",
                      Some_college="5",
                      Associate_degree="6",
                      College_graduate="7",
                      Some_graduate_education="8",
                      Graduate_degree="9",
                      Dont_know="88")
  g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))

  new.d <- data.frame(new.d, g4c)
  new.d <- apply_labels(new.d, g4c = "education-mother")
  temp.d <- data.frame (new.d, g4c)  
  
  result<-questionr::freq(temp.d$g4c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
n % val%
Grade_school_or_less 24 11.9 13.7
Some_high_school 19 9.5 10.9
High_school_graduate_GED 54 26.9 30.9
Vocational_school 7 3.5 4.0
Some_college 18 9.0 10.3
Associate_degree 10 5.0 5.7
College_graduate 14 7.0 8.0
Some_graduate_education 2 1.0 1.1
Graduate_degree 7 3.5 4.0
Dont_know 20 10.0 11.4
NA 26 12.9 NA
Total 201 100.0 100.0

G5: Job

  • G5. Which one of the following best describes what you currently do?
    • 1=Currently working full-time
    • 2=Currently working part-time
    • 3=Looking for work, unemployed
    • 4=Retired
    • 5=On disability permanently
    • 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
    • 7=Volunteer work/work without pay
    • 8=Other
  g5 <- as.factor(d[,"g5"])
  # Make "*" to NA
g5[which(g5=="*")]<-"NA"
  levels(g5) <- list(full_time="1",
                     part_time="2",
                     unemployed="3",
                     Retired="4",
                     disability_permanently="5",
                     disability_for_a_time="6",
                     Volunteer_work="7",
                     Other="8")
  g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))

  new.d <- data.frame(new.d, g5)
  new.d <- apply_labels(new.d, g5 = "job")
  temp.d <- data.frame (new.d, g5)  
  
  result<-questionr::freq(temp.d$g5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g5")
g5
n % val%
full_time 64 31.8 33.5
part_time 7 3.5 3.7
unemployed 1 0.5 0.5
Retired 91 45.3 47.6
disability_permanently 24 11.9 12.6
disability_for_a_time 1 0.5 0.5
Volunteer_work 1 0.5 0.5
Other 2 1.0 1.0
NA 10 5.0 NA
Total 201 100.0 100.0

G5 Other: job

g5other <- d[,"g5other"]
  new.d <- data.frame(new.d, g5other)
  new.d <- apply_labels(new.d, g5other = "g5other")
  temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G5 Other")
G5 Other
n % val%
100% physically and mentally disabled. 1 0.5 12.5
Also perform volunteer work each week 1 0.5 12.5
Covid 19 1 0.5 12.5
Covid-19. Work part-time for SFUSD schools. 1 0.5 12.5
Disabled from birth, intellectually disabled. 1 0.5 12.5
Health care/hospital shipping and receiving 1 0.5 12.5
On disability. 1 0.5 12.5
Self employed 1 0.5 12.5
NA 193 96.0 NA
Total 201 100.0 100.0

G6: Health insurance

  • G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
    • G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
    • G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
    • G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
    • G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
    • G6_5: 1= Medicaid or other state provided insurance
    • G6_6: 1=Medicare/government insurance
    • G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
    • G6_8: 1=I do not have any medical insurance
  g6_1 <- as.factor(d[,"g6_1"])
  levels(g6_1) <- list(Insurance_employer="1")
  new.d <- data.frame(new.d, g6_1)
  new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
  temp.d <- data.frame (new.d, g6_1)  
  result<-questionr::freq(temp.d$g6_1,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_employer 83 41.3 100
NA 118 58.7 NA
Total 201 100.0 100 
  g6_2 <- as.factor(d[,"g6_2"])
  levels(g6_2) <- list(Insurance_family="1")
  new.d <- data.frame(new.d, g6_2)
  new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
  temp.d <- data.frame (new.d, g6_2)  
  result<-questionr::freq(temp.d$g6_2,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
n % val%
Insurance_family 40 19.9 100
NA 161 80.1 NA
Total 201 100.0 100 
  g6_3 <- as.factor(d[,"g6_3"])
  levels(g6_3) <- list(Insurance_insurance_company="1")
  new.d <- data.frame(new.d, g6_3)
  new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
  temp.d <- data.frame (new.d, g6_3)  
  result<-questionr::freq(temp.d$g6_3,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
n % val%
Insurance_insurance_company 9 4.5 100
NA 192 95.5 NA
Total 201 100.0 100 
  g6_4 <- as.factor(d[,"g6_4"])
  levels(g6_4) <- list(Insurance_exchange="1")
  new.d <- data.frame(new.d, g6_4)
  new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
  temp.d <- data.frame (new.d, g6_4)  
  result<-questionr::freq(temp.d$g6_4,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
n % val%
Insurance_exchange 2 1 100
NA 199 99 NA
Total 201 100 100 
  g6_5 <- as.factor(d[,"g6_5"])
  levels(g6_5) <- list(Medicaid_state="1")
  new.d <- data.frame(new.d, g6_5)
  new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
  temp.d <- data.frame (new.d, g6_5)  
  result<-questionr::freq(temp.d$g6_5,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
n % val%
Medicaid_state 26 12.9 100
NA 175 87.1 NA
Total 201 100.0 100 
  g6_6 <- as.factor(d[,"g6_6"])
  levels(g6_6) <- list(Medicare_government="1")
  new.d <- data.frame(new.d, g6_6)
  new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
  temp.d <- data.frame (new.d, g6_6)  
  result<-questionr::freq(temp.d$g6_6,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
n % val%
Medicare_government 73 36.3 100
NA 128 63.7 NA
Total 201 100.0 100 
  g6_7 <- as.factor(d[,"g6_7"])
  levels(g6_7) <- list(VA_Military="1")
  new.d <- data.frame(new.d, g6_7)
  new.d <- apply_labels(new.d, g6_7 = "VA_Military")
  temp.d <- data.frame (new.d, g6_7)  
  result<-questionr::freq(temp.d$g6_7,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
n % val%
VA_Military 11 5.5 100
NA 190 94.5 NA
Total 201 100.0 100 
  g6_8 <- as.factor(d[,"g6_8"])
  levels(g6_8) <- list(Do_not_have="1")
  new.d <- data.frame(new.d, g6_8)
  new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
  temp.d <- data.frame (new.d, g6_8)  
  result<-questionr::freq(temp.d$g6_8,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
n % val%
Do_not_have 1 0.5 100
NA 200 99.5 NA
Total 201 100.0 100

G7: Income

  • G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
    • 1=Less than $15,000
    • 2=$15,000 to $35,999
    • 3=$36,000 to $45,999
    • 4=$46,000 to $65,999
    • 5=$66,000 to $99,999
    • 6=$100,000 to $149,999
    • 7=$150,000 to $199,999
    • 8= $200,000 or more
  g7 <- as.factor(d[,"g7"])
  # Make "*" to NA
g7[which(g7=="*")]<-"NA"
  levels(g7) <- list(Less_than_15000="1",
                     Between_15000_35999="2",
                     Between_36000_45999="3",
                     Between_46000_65999="4",
                     Between_66000_99999="5",
                     Between_100000_149999= "6",
                     Between_150000_199999="7",
                     More_than_200000="8")
  g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))

  new.d <- data.frame(new.d, g7)
  new.d <- apply_labels(new.d, g7 = "income")
  temp.d <- data.frame (new.d, g7)  
  
  result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g7")
g7
n % val% %cum val%cum
Less_than_15000 21 10.4 11.3 10.4 11.3
Between_15000_35999 24 11.9 12.9 22.4 24.2
Between_36000_45999 17 8.5 9.1 30.8 33.3
Between_46000_65999 15 7.5 8.1 38.3 41.4
Between_66000_99999 31 15.4 16.7 53.7 58.1
Between_100000_149999 44 21.9 23.7 75.6 81.7
Between_150000_199999 19 9.5 10.2 85.1 91.9
More_than_200000 15 7.5 8.1 92.5 100.0
NA 15 7.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

G8: # people supported by income

  • G8. In the last calendar year, how many people, including yourself, were supported by your family income?
    • 1=1
    • 2=2
    • 3=3
    • 4=4
    • 5=5 or more
  g8 <- as.factor(d[,"g8"])
  # Make "*" to NA
g8[which(g8=="*")]<-"NA"
  levels(g8) <- list(One="1",
                     Two="2",
                     Three="3",
                     Four="4",
                     Five_or_more="5")
  g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))

  new.d <- data.frame(new.d, g8)
  new.d <- apply_labels(new.d, g8 = "people supported by income")
  temp.d <- data.frame (new.d, g8)  
  
  result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g8")
g8
n % val% %cum val%cum
One 61 30.3 31.9 30.3 31.9
Two 76 37.8 39.8 68.2 71.7
Three 27 13.4 14.1 81.6 85.9
Four 15 7.5 7.9 89.1 93.7
Five_or_more 12 6.0 6.3 95.0 100.0
NA 10 5.0 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

G9: Worry about finance

  • G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
      1. During young adult life (up to age 30):
      1. Age 31 (up to just before prostate cancer diagnosis):
      1. Current (from prostate cancer diagnosis to present):
      • 1=Not at all worried
      • 2=A little worried
      • 3=Somewhat worried
      • 4=Very worried
  g9a <- as.factor(d[,"g9a"])
  # Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
  levels(g9a) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9a)
  new.d <- apply_labels(new.d, g9a = "young adult life")
  temp.d <- data.frame (new.d, g9a)  
  result<-questionr::freq(temp.d$g9a,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
n % val%
Not_worried 95 47.3 49.0
A_little_worried 42 20.9 21.6
Somewhat_worried 30 14.9 15.5
Very_worried 27 13.4 13.9
NA 7 3.5 NA
Total 201 100.0 100.0 
  g9b <- as.factor(d[,"g9b"])
    # Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
  levels(g9b) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9b)
  new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
  temp.d <- data.frame (new.d, g9b)  
  result<-questionr::freq(temp.d$g9b,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
n % val%
Not_worried 99 49.3 51.6
A_little_worried 46 22.9 24.0
Somewhat_worried 41 20.4 21.4
Very_worried 6 3.0 3.1
NA 9 4.5 NA
Total 201 100.0 100.0 
  g9c <- as.factor(d[,"g9c"])
    # Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
  levels(g9c) <- list(Not_worried="1",
                      A_little_worried="2",
                      Somewhat_worried="3",
                      Very_worried="4")
  new.d <- data.frame(new.d, g9c)
  new.d <- apply_labels(new.d, g9c = "current")
  temp.d <- data.frame (new.d, g9c)  
  result<-questionr::freq(temp.d$g9c,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
n % val%
Not_worried 96 47.8 50.5
A_little_worried 33 16.4 17.4
Somewhat_worried 34 16.9 17.9
Very_worried 27 13.4 14.2
NA 11 5.5 NA
Total 201 100.0 100.0

G10:Own or rent a house

  • G10. Is the home you live in:
    • 1=Owned or being bought by you (or someone in the household)?
    • 2=Rented for money?
    • 3=Other
  g10 <- as.factor(d[,"g10"])
  # Make "*" to NA
g10[which(g10=="*")]<-"NA"
  levels(g10) <- list(Owned="1",
                     Rented="2",
                     Other="3")
  g10 <- ordered(g10, c("Owned","Rented","Other"))

  new.d <- data.frame(new.d, g10)
  new.d <- apply_labels(new.d, g10 = "Own or rent a house")
  temp.d <- data.frame (new.d, g10)  
  
  result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g10")
g10
n % val% %cum val%cum
Owned 116 57.7 59.8 57.7 59.8
Rented 74 36.8 38.1 94.5 97.9
Other 4 2.0 2.1 96.5 100.0
NA 7 3.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

G10 Other: Own or rent a house

g10other <- d[,"g10other"]
  new.d <- data.frame(new.d, g10other)
  new.d <- apply_labels(new.d, g10other = "g10other")
  temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
  kable(result, format = "simple", align = 'l', caption = "G10 Other")
G10 Other
n % val%
Rent 1 0.5 20
Rented 2 1.0 40
Roommate situation. 1 0.5 20
This house is hers-not my house. Renter. 1 0.5 20
NA 196 97.5 NA
Total 201 100.0 100

G11:Lose current sources

  • G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
    • 1=Less than 1 month
    • 2=1 to 2 months
    • 3=3 to 6 months
    • 4=More than 6 months
  g11 <- as.factor(d[,"g11"])
  # Make "*" to NA
g11[which(g11=="*")]<-"NA"
  levels(g11) <- list(Less_than_1_month="1",
                     One_to_two_month="2",
                     Three_to_six_month="3",
                     More_than_6_months="4")
  g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))

  new.d <- data.frame(new.d, g11)
  new.d <- apply_labels(new.d, g11 = "ose current sources")
  temp.d <- data.frame (new.d, g11)  
  
  result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
  kable(result, format = "simple", align = 'l', caption = " g11")
g11
n % val% %cum val%cum
Less_than_1_month 27 13.4 14.4 13.4 14.4
One_to_two_month 39 19.4 20.7 32.8 35.1
Three_to_six_month 40 19.9 21.3 52.7 56.4
More_than_6_months 82 40.8 43.6 93.5 100.0
NA 13 6.5 NA 100.0 NA
Total 201 100.0 100.0 100.0 100.0

G12: Today’s date

  • G12. Please enter today’s date.
  g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
  new.d <- data.frame(new.d, g12)
  new.d <- apply_labels(new.d, g12 = "today’s date")
  #temp.d <- data.frame (new.d.1, g12) 
  
  summarytools::view(dfSummary(new.d$g12, style = 'grid',
                               max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
No Variable Label Stats / Values Freqs (% of Valid) Graph Missing
1 g12 [labelled, Date] today’s date
min : 2019-12-12
med : 2020-07-28
max : 2020-12-27
range : 1y 0m 15d
 145 distinct values 4 (2.0%)

Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09